{"as_of":"2026-08-20T17:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eb8c07234149241c63a9ea89114e288e73f9cda3220e948bd0cd27fd03e1407a","coverage":[{"denominator":110,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T19:16:42.139096Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.00602/citation-record","integrity":"/paper/2606.00602/integrity","json":"/paper/2606.00602/citation-record.json","paper":"/paper/2606.00602"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Simcrop: Radiograph representation learning with similarity-driven cross-granularity pre-training,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:e4f50f078caa3aaa1b53f03b3a6614ad41a43c45aca13f8f03e7cde21514657b","observation_id":"9b7cc976-588b-44bc-88a6-98d8c799a8da","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Unimiss+: Universal medical self-supervised learn- ing from cross-dimensional unpaired data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:7a02e2371b045fbda8c3d1994f694a5660bf514d0909315719b83787c2431d6d","observation_id":"8714eff5-c1ee-4ac0-bbf8-64577e8339b0","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Medical image segmentation review: The success of u-net,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:c2272b6bcd2aac56a4570e58e6645ac3339ca55c422394a6a538e85fa4c97ccd","observation_id":"c710ff5d-d0d0-43ce-8552-1ad49913ebe6","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Visionunite: A vision-language foundation model for ophthalmology enhanced with clinical knowledge,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:ffd13e41dedcc9e8e63c2c995697f497ac9ae8f18262ae0f33041d4bbae0ef60","observation_id":"55cfe07e-9d34-40a4-ac07-2266d6c07fa8","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Pathway-aware multimodal transformer (pamt): Integrating pathological image and gene expression for inter- pretable cancer survival analysis,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:ff0b1825374bb1e4704d4e38c43ef31ef4d244d38b672fb2e4759e131e4735b2","observation_id":"d30e09c6-f585-43f6-8dba-a69670e1a6da","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:0a81955cd6679aa8b3fd75a776c3f597a48b055e306af797d9132f12aabd9560","observation_id":"a2c51885-0bcb-4069-a4c2-2e4dcda62086","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Minimizing estimated risks on unlabeled data: A new formulation for semi-supervised medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:1c3b56b6cff402757baa68e068c71af95669c44e24e70261251f0d4145e2c161","observation_id":"d880c931-5a4d-49a3-afec-7f2fa6983bfc","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Deep transfer learning based classification model for covid-19 disease,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:edc09f3b7265e378385fd2239eb0b938c24e59f6692e925d929c332dc0a9a46a","observation_id":"568d4def-e59b-4f16-bc4b-b4073cebbe31","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Recent advances and clinical applications of deep learning in medical image analysis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:32a1962dc988449eb2ed4034076ab294c0f7b4ca996fcfca28ef6cb26086e1b4","observation_id":"e9a9d5bd-4838-47db-84e0-299f9c1eab0c","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Diagnose like a radiologist: Hybrid neuro- probabilistic reasoning for attribute-based medical image diag- nosis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:98dc299a2c0a34f560d1fcc4087aa1a2a14f0fa14d60bd1fe22d159232eeefa9","observation_id":"33f6f518-4aa5-494c-9add-b6acb02df617","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Homeomorphism prior for false positive and nega- tive problem in medical image dense contrastive representation learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:b22ceeef9acdb3dfcbc3a6cb8b2666cf642c22d77a7edbcdb209fe11fd6e7437","observation_id":"676dd4f3-d243-4e0e-94ea-7dc96b1274a6","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Generalized radiograph representation learn- ing via cross-supervision between images and free-text radiology reports,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f1a3c9be001a5e771ae28a997eaac6428bf9d766338a0d1291b14c55d36af45a","observation_id":"82decae9-fa51-49fe-b746-8b0204568c39","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Knowledge- enhanced visual-language pre-training on chest radiology im- ages,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:2a2e24f30b6ff38c41d78ab0ae7e143ebb9812a7f1f6018bb57948741721b8bf","observation_id":"9cc88a43-fed2-467a-b50a-12344f9a5737","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Towards generalist foundation model for radiology by leveraging web-scale 2d&3d medical data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:e0b02723b5aa1a84aacfa5a70ec0f5246fbeac78c76c050f5b77d79115d5c0d5","observation_id":"98c4a3d7-5ee5-485a-a70f-c4786eaf2b7d","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"A unified visual information preservation framework for self-supervised pre-training in medical image analysis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:97bf44f3a1b93123f321808f46029d3250264f964975f407ed56b481f63ae83d","observation_id":"49b13af0-2ce1-46ec-bae2-a0ea73bd7f06","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"A medical multimodal large language model for future pandemics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:4e9920a5c5047eae0f2df08510af1b5e264fa1d43db85dcacb7f71ec2fa19c08","observation_id":"44147b14-2691-4459-b386-d95e4f771289","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Abdomenct-1k: Is abdominal organ segmentation a solved problem?","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:8e51a07fb0614fff6626b4da15f4620aba649eb54f2bf1dd460184613ad2a7cd","observation_id":"5c6c356b-7b59-4fe2-9d3f-50fdbde2bedf","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Development of a large-scale medical visual question-answering dataset,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:0a8da2ee584eec9c1ed450eafd9570ca1f10819007c66c5ec63aea3e23944473","observation_id":"dd8317ba-8bbb-4d6f-bcd7-18388815536e","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Large-scale long-tailed disease diagnosis on radiology images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:1c450f5e580e06840c5b34d48e97d21543818446de375b0fda3ef1345fbae2da","observation_id":"40aca9e3-39da-4150-bd05-cbe844abc01b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Medical multimodal multitask foundation model for lung cancer screening,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:145c8cca6b362b3e1eeb8d5cedb4f979ad9545ee6d1554920696295274a77f75","observation_id":"81528565-80c3-49c4-b306-12b9986de5bf","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Hi-end-mae: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f03bd87ebc65a1acac2ee0ff0980fc714d51b2c340e751d84c5221560fe13f4a","observation_id":"8680fee0-ef74-4e26-b49a-c1418b1fc6ae","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Contrastive learning of medical visual repre- sentations from paired images and text,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:9c1ad7d0b3a164bffb3277fb5d3df5c2287a0c5a79a1e41df65d627ace05dc97","observation_id":"6f2e7cd6-9fbb-40b5-85be-d1e0e034048f","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Gloria: A multimodal global-local repre- sentation learning framework for label-efficient medical image recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:46b5e76ef47d125772a2ebe9c093c2b68eeac315375d8d0e4893e52ada0530da","observation_id":"6e92ca15-a318-41bf-90f7-5b1a11203825","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Multi-granularity cross-modal align- ment for generalized medical visual representation learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:02e8c2f1ddf172793e3ce0601e282c024b784ad4d2cab95f9ec48c66085ccbdb","observation_id":"b1c639fd-c07b-429a-b38d-b2aa1f5cfb2b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"MedCLIP: Contrastive learning from unpaired medical images and text,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:9a91bccfd7dd2a4481aee756cef92703ce08ed23b2dc43d67f6f6853c3176161","observation_id":"643709c1-daee-413d-b4f1-ea45625b477c","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Medklip: Medical knowledge enhanced language- image pre-training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:6216bfe3d2f7b7260406abcb874c27f135e1159c65def0e2ef9d7036f2031f7f","observation_id":"6ed3b470-c64a-42eb-a52e-33badbdea6ad","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Expert-level detection of pathologies from unan- notated chest x-ray images via self-supervised learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:b839a2da26a8d6dd34e47a6662885d5596e2fd9dc375e3e0e9b4671e3fc896eb","observation_id":"0c7c60d8-d612-409b-967d-d76d06b9b06d","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Advancing radiograph representation learning with masked record modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:1c906d37ccbb4d8f9ea71e638aa0fb8bd21975154f0635953bab2fc899f67d00","observation_id":"067efde0-5110-4fc6-9d85-6c2631adaef7","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Mlip: Enhancing medical visual representation with divergence encoder and knowledge-guided contrastive learn- ing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:8758172bd0a7f560a780e57582ddb04ccf40d7d2bd7e2c875dfd2677865e4fb0","observation_id":"21dfd5b2-8e08-4f3d-81fc-bc9c45397161","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Enhancing representation in radiography- reports foundation model: A granular alignment algorithm using masked contrastive learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:65cf939ade0bae36ec4dc935d17cb4c6b06b2bc201c3623e9f8b1ea4d0489fd3","observation_id":"ef3c44bc-df3a-4ec3-912b-030cd0b81cee","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Ecamp: Entity-centered context-aware medical vision language pre-training,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:55d67c029662f4d6937a7d74f4d0cb86900a49fdd36562d413e644f5f3bbd1a4","observation_id":"7db0ef59-d69d-40d9-baeb-8ec095ee4f4c","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Efficient medical vision-language alignment through adapting masked vision models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:5da9ff7347caf66d4bcebb86fd1328c590dcab2dafabe9e928ab1c8ee8eae3e6","observation_id":"c989c556-97bf-444e-827b-e774bf09694d","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Bootstrapping chest ct image understanding by distilling knowledge from x-ray expert models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:26cf64d286177071d5ce70b22d5fcc41d1e16295fe604ed0d78054b328a9d518","observation_id":"ec5bcbde-986b-4e5d-b725-209c7faa9174","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Merlin: a computed tomography vision– language foundation model and dataset,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:2c08fece507a305688eadae4cab2edb168ac558519f6e11b0ef45404e4eae6f3","observation_id":"52306f51-7557-40d0-81c1-3f1c437b5d0e","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Large-scale 3d medical image pre-training with geometric context priors,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:1da937831023d742c04225b98674b27fd92cb67ee0eea04d07d13a4cb36c86a9","observation_id":"11df3fcb-a0cd-48fe-af4d-b8338dd07d56","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Generalist foundation models from a mul- timodal dataset for 3d computed tomography,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:07d569625403014e4c20c5b3369b0c155eabf06a2e289a05552a2b465d4ee377","observation_id":"037c7a4d-ae4b-45a2-b74a-2f77d650b7a9","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomography volumes,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:43edd77de5e727e68024fe489fbd12c129e7bd82970e5ded59d5424366074215","observation_id":"124ab31d-b74a-4948-893e-a3d96b6fd817","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.01174","last_updated":"2020-06-05T12:53:43Z","snapshot_observed_at":"2026-08-12T06:03:57.009788Z","submitted_at":"2020-06-01T18:06:21Z","title":"BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients","version":3},"cited_work":{"arxiv_id":"2006.01174","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.01174","snapshot_observed_at":"2026-06-28T19:22:34.667390Z","title":"BIMCV COVID-19+: A large annotated dataset of RX and CT images from COVID-19 patients","venue":null,"work_id":"9b5382e5-2fce-4a72-bbb6-5ffa79bb951f","year":2006},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"cited_paper":"/paper/2006.01174","citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:76814063fe202e6f233c9ab7a40321cd0b172c100be87bccdbb165c0bd6b2856","observation_id":"7c4f571e-fac3-40ad-a006-1f027f5c6f2a","resolution":{"observed_at":"2026-06-28T19:22:34.668852Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Large-scale and fine-grained vision-language pre- training for enhanced ct image understanding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:3ee80e85a908d7ab7f2870312ecbad7d32ae09581900d23da4e7a832409d287a","observation_id":"b7c6815a-065f-43e1-b7ad-0acb954d3d31","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Boosting vision semantic density with anatomy normality modeling for medical vision-language pre-training,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:d14edc3636e80d1a7309f97d8d00e97bd46429df73e0e68266b58c2c47e22e37","observation_id":"a674b6a6-fd94-4ae5-b6a2-96ca36015c58","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2404.15272","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T16:29:57.781680Z","title":"arXiv preprint arXiv:2404.15272 (2024)","venue":null,"work_id":"681fa3a5-4bb2-4cfa-8bc2-616a24654f60","year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:24e8199d17393ab25a63988d0249903186924b8a15fa4ab4742fd1a99a7263e6","observation_id":"f2e685c6-40c0-4bef-990b-210b140af7a9","resolution":{"observed_at":"2026-06-28T19:22:34.670630Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00578","last_updated":"2024-03-31T06:55:12Z","snapshot_observed_at":"2026-08-18T17:14:44.330300Z","submitted_at":"2024-03-31T06:55:12Z","title":"M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models","version":1},"cited_work":{"arxiv_id":"2404.00578","doi":"10.48550/arxiv.2404.00578","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00578","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"M3d: Advancing 3d medical image analysis with multi-modal large language models","venue":"arXiv (Cornell University)","work_id":"7bc29714-95ad-4249-a8ca-eabb142c8078","year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"cited_paper":"/paper/2404.00578","citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:727f8cde02e39893b937e06fd808919d71033507843ee53d29314da41df05995","observation_id":"a0f3b2d1-8329-4eb8-8fb6-8a321e9d304f","resolution":{"observed_at":"2026-06-28T19:22:34.679589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"T3d: Advancing 3d medical vision-language pre- training by learning multi-view visual consistency,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:16345be874d8556d2e78461570526563dcf8984495103c6e27ee978d3023e21e","observation_id":"3eba48ea-59f7-439b-8d5e-0bb0cee7b2e2","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Radzero3d: Bridging self-supervised video models and medical vision-language alignment for zero-shot chest ct interpretation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:dd423e09f9f954ed52214e8c01231574025434641f55179655503dba60eff690","observation_id":"f75a6ca7-b7bb-4855-8c6f-18248d13f64f","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Large-vocabulary segmentation for medical im- ages with text prompts,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:3d36fac942cf25e541fc561491a8795f409f9b7ab8c9b6365e84270a3ff8f001","observation_id":"28965099-569b-4959-a99e-0a7bd6807c1b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Totalsegmentator: robust segmentation of 104 anatomic structures in ct images,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:e4f29c915f67667446c4e7fa6f2e18c55d05eefd35f04945a02dcf3d73afcee1","observation_id":"164b36da-234d-41d1-bea8-c34c7ba3f3e6","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Towards scalable language-image pre-training for 3d medical imaging,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:5ea4b380520bb5084344884a96abd451e1e31b9f3f0518909d572960aa489bbf","observation_id":"57e85d97-7040-41cc-9367-49f197bffde5","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Multi-modal masked autoencoders for medical vision-and-language pre-training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:1dccc110110dc5a645967da871470bfc3e22986d9da8cbd53d4b0870b661a71d","observation_id":"5b09a5cf-ce32-40c7-9490-f97ff36011fb","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:23ff81a4623c4f250c0d14607e39ca05b030ffc860df51fe8b3699a892ede948","observation_id":"0603080e-01fb-4857-8e7f-95e3cb50a514","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Scaling up visual and vision-language representation learning with noisy text supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:c88801c70d010a218abd2ee6a50d2891286732c24cc507876a3f450fc149fcf2","observation_id":"00b249eb-b5b1-4e92-8288-45b47de27adc","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01917","last_updated":"2022-06-14T00:48:04Z","snapshot_observed_at":"2026-08-13T15:12:42.567441Z","submitted_at":"2022-05-04T07:01:14Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","version":2},"cited_work":{"arxiv_id":"2205.01917","doi":"10.48550/arxiv.2205.01917","metadata_source":"pith","pith_arxiv_id":"2205.01917","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","venue":"cs.CV","work_id":"5dd5bf10-d548-40ff-9b6c-6735129b27ee","year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"cited_paper":"/paper/2205.01917","citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:6a613fab3b746d8d2a668ec13476c5d66a1d284c474e9105defe27ab8484520e","observation_id":"2b9cc321-b158-47dc-8a17-c23aa2e682e5","resolution":{"observed_at":"2026-06-28T19:22:34.676717Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:43.398968+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:43.398968+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Grounded language-image pre-training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:ccdfc5eb9d1223adb9d7634e61fe5f32f9f7467b38b0277a678703bb6644c84c","observation_id":"7fb3388e-3ef4-4166-be9e-3f6ff972eae7","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Scaling language-image pre-training via masking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:d4460d7c3f43ed3b00ac587a7351e7e2c553cb935fca3af82b6828edd463e781","observation_id":"4d3971ab-c827-43d1-bd0c-ac71bb698eae","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Flamingo: a visual language model for few- shot learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:070ad2078754a0e567e803ba45733e488407ce41f594d91c8ce642e4fb9cf4b1","observation_id":"6d57ed1e-8ac8-438d-b420-a35b4274ce1f","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:176a16185f1fe4355b7ab98f6195bcb32743e9b6c3497eaed86e2e8ce8caef8b","observation_id":"bec95b64-3523-4755-9fc6-e5263fe28fc3","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:c52827be5958e2e8ce66fc5f9fdf18eaa46f655e059509e02282d760cc445c52","observation_id":"7f25424a-0020-44ee-aea1-00d303f47df3","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Sigmoid loss for language image pre-training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:35dacd2bab63aec1e541542165edf432cb7c1af25e7cb9d502c78645d3d1ea24","observation_id":"3dda4d07-3dd8-4381-b526-537661b60ca2","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f454e29525a09936d6251e36e49b4895072142abae3bca31b7c1a2e73ddbdadf","observation_id":"87217183-9072-4b36-bed8-fa5606673683","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Vl-bert: Pre-training of generic visual-linguistic representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:7b3c60b88667b767f367fe1c7ade011326406834e76b1009a770ffcd85382a75","observation_id":"9236816a-74e2-477d-be52-be34046e5e5c","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Beit: Bert pre-training of image transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:9ab7018a2a14990ddb4196185c459f3ac6b45d430a91f643159b845412001fa7","observation_id":"009af067-2f8b-4c24-b2cc-e2ddbf93da50","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Flava: A foundational language and vision alignment model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f46faf5314d98487670be59992ef04b093bed0470cb5f89da9f59cce65905666","observation_id":"18333284-ce6a-453d-a6f9-b625ab19f8c6","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Image as a foreign language: Beit pretraining for vision and vision-language tasks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:669f812aaa381e88cd70907fa045026d2c5ead1a6fb75ee0054eb0fd9467ed12","observation_id":"8ba524ab-144d-4979-825b-9de19472cfeb","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Valor: Vision-audio-language omni-perception pre- training model and dataset,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:cdbb0f69d28eb48e1c369a2a6d050841961594de23182fef6434dde7a599c983","observation_id":"e45e6947-dc79-400a-9e21-ef27530ca17f","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Unsupervised pre-training with language-vision prompts for low-data instance segmentation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:85bd18f5844f6fd442dd994d14324c28affc93591f877d6021485b7fa5b3489f","observation_id":"042702f0-d632-4f05-bf4f-2eb3e4b47cbc","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:b06da9e01d3f114b4da712ff0939a100a74e8edd77f15d96e61f3d759a80b152","observation_id":"df9e98cd-8548-4996-b504-dab10d36e730","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Eva-x: A foundation model for general chest x- ray analysis with self-supervised learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:50f8a3c312cfaedcbefb6be5ebdce6dc07f52462deb89f459092d31edef4d536","observation_id":"311f9d31-ddf7-42d7-b57f-906729b2596e","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Med-unic: Unifying cross-lingual medical vision- language pre-training by diminishing bias,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:eb2ec9db8884e3f895b22e204f0759ab34a203b063940cf4cd007e8be7e2805c","observation_id":"286d2caa-cf30-4fb4-ade3-abce9f024d46","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Rethinking masked image modeling for medical image representation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:28c561b81ef10ae1fcd7c657ffe2efcefb2089c4c9a78cb8c89981461432d5e4","observation_id":"310013d8-5d4b-46d3-a84a-3e427fc96ea3","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Voco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:49ddda4d806aa186a6e60ae5a3ed742058147ad4eb0f25c00ae5de590864ed6a","observation_id":"4dc28bbe-f73a-4a55-8cef-2781124057c0","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Mim: Mask in mask self-supervised pre-training for 3d medical image analysis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:995830e60144f590e2e9e92157ef5d0d21f61c4943581596bd8033b2076531dc","observation_id":"63a82504-1f57-4cca-a30a-39010cb46504","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Enhancing the vision–language foundation model with key semantic knowledge-emphasized report refine- ment,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:659e8d30e1eca9c0a1639fce6671b48492c3f20ccfa42cd8f1944b863ad24f98","observation_id":"36bc480e-4bad-48cc-a6fb-ea5e2c8d2d31","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Imitate: Clinical prior guided hierarchical vision- language pre-training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:8836de0b2be68c9ce82fc8fc674fe8aa3faf2fd2eb016a1990ff16445b780698","observation_id":"0569bf8b-1282-4300-8b14-90275f352356","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Multi-grained vision-and-language model for medical image and text alignment,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:bef9addda7a76b9c6c26b0e996d14c9aa71c18b0f6aa882500a3c690eabafe71","observation_id":"1b1facda-13f0-4391-859e-2d05b7227d55","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Semantic-aware hard negative mining for medical vision-language contrastive pretraining,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:d4727ddc20f48fd3aeda54a31394871a99972cd401520bdd4719b6688a639444","observation_id":"b196004a-3dea-47e7-a4f9-30560e77d791","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Prior: Prototype representation joint learning from medical images and reports,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:d1955f006bba2aeda8b07da330940150f46e18883fdfabe8b52da99b6bd11a84","observation_id":"a8b3bdb5-045a-4277-b3a5-d97a69684233","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"G2d: From global to dense radiography representa- tion learning via vision-language pre-training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:0ef1f624d5da353f2749d4409d2bc837da933addfb490ced1c0c00969af1c0c1","observation_id":"fb7bf038-5c74-40dd-8f60-3d62c6aa3967","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"X-ray computed tomography,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:d2827d69eb480bce3d2a5c6ad22a5ddf15f8924b47d180c630cb11ba35a469eb","observation_id":"408684e9-2a42-4cf3-b8aa-82a9c6b8b7c3","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Geometric visual similarity learning in 3d medical image self-supervised pre-training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f23e9560046519c6907a24657ff1c882336f1d36dc8cdf37f615d95ae8264415","observation_id":"074b7a76-00eb-4242-ad78-06bb5ab58eda","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Unified medical image pre-training in language- guided common semantic space,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:fb6e8797bf59d1bbb97ec125c30cd311f7d48a250d4eae4621b0b922d5920301","observation_id":"7f7d3196-1ce8-4061-abc6-f8d8bb42f14b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Mg-3d: Multi-grained knowledge-enhanced vision- language pre-training for 3d medical image analysis,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f9e46eaab52248761a4c1a153c3af0ef972aed0bf9d24d67ec90c8ae5550d904","observation_id":"9f5d8955-6ce0-46b8-8400-636f7e46c0c3","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.03800","last_updated":"2025-09-04T01:28:44Z","snapshot_observed_at":"2026-08-17T12:18:24.366486Z","submitted_at":"2025-09-04T01:28:44Z","title":"MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting","version":1},"cited_work":{"arxiv_id":"2509.03800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.03800","snapshot_observed_at":"2026-06-28T19:22:34.657932Z","title":"Medvista3d: Vision-language modeling for reducing diagnostic errors in 3d ct disease detection, understanding and reporting,","venue":null,"work_id":"a5511f07-ba18-4865-b330-19daca5fc28e","year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"cited_paper":"/paper/2509.03800","citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:29d01d4a25b54d181ef16d86803e7a3986c1093ac36b2f13f48aaf67d7ff0a39","observation_id":"e8ecbf8a-69eb-48e9-9d43-16ea5263acd2","resolution":{"observed_at":"2026-06-28T19:22:34.659378Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:3fa15aa854e448eb26bdbb13f7582dcd94af599136f887810154f185440eea20","observation_id":"f2cf9b25-91b6-4fd5-b060-0948cb7b7563","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Bert: Pre-training of deep bidirectional transform- ers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:8f4514fe2454bc3f21c54602fc12bcdb0bccd7ce23f18ace7f72b26a7a41c5cd","observation_id":"02ec643f-b411-4bf4-96c8-df1769b4d130","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Masked autoencoders are scalable vision learners,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:8c2bd44dcc87edb2c9047edd6581b3747efca393084263110f2a424a1e82feec","observation_id":"fb934d60-5818-4fd4-8dac-0541c700680c","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Self pre-training with masked autoencoders for medical image classification and segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:ea83dad61559603b354619b9bf7abd4c021d4d3343e442add32b7f0873ae7280","observation_id":"2e1b0f50-9ff6-467b-afa4-f9b6cc56fca0","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Benchmarking deep learning models and auto- mated model design for covid-19 detection with chest ct scans,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:b697663c8c0b7824afb027c7bb1dfbedf680afb79782e55e079f5b893a99553e","observation_id":"5ffd920c-9d3b-4633-852b-bf999c6be58b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"The medical segmentation decathlon,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:2edfa73e2dab7e3b4d115bce9336c9898165cd6beb5ff3cc177483a9dafd810c","observation_id":"45dd34b9-d429-4b14-a9a0-8f9cadfb3207","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Rapid artificial intelligence solutions in a pan- demic—the covid-19-20 lung ct lesion segmentation challenge,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:16fd2a631e92db8bf8d5c86da6490652c60d1b94cd79ad41e16a3325e4de3665","observation_id":"4aad0136-f73e-4846-964e-3e2631a81a65","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Segthor: Segmentation of thoracic organs at risk in ct images,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:abcf6fb2ba2a24ef999806c9b74df41f658d022a645f52e8004d998bf11be511","observation_id":"4f6012bf-0ae6-4d4a-9d1d-805d3617e424","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:0a3feaa13a989ff64165809295719eec5854abda12468e2476626e478318c94e","observation_id":"b55eddc7-779d-4fe9-afd5-09f47d1bcd54","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:dea0e364f046f322eb307cf870723f74205810a74dc99fa06753072d38799e9c","observation_id":"d4762ad5-fb62-46d0-a994-f4f38445a716","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the prob- lem solved?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:884f0ecd677c93f4cb4fe03d380179ffdaa05aa8d2d02d41803f55b87a3e50d0","observation_id":"a57469ba-9ff8-41f7-8c15-5687b1ce219b","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Work like a doctor: Unifying scan localizer and dynamic generator for automated computed tomography report generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:7a2f93af34a35a21bd686dacbf09ae7f3684d4daae62d7610ace5ffa324de354","observation_id":"573916e0-e3a2-4a7e-bc1c-040e0243ab85","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:7fcd83632b8bdea83477afcbb395bb0141dd58d987393f9e5e89116f02b5bfc3","observation_id":"015f544e-cc8c-4b78-902d-d96eaecd52b1","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10798","last_updated":"2023-11-17T07:28:16Z","snapshot_observed_at":"2026-08-18T08:20:51.357469Z","submitted_at":"2023-11-17T07:28:16Z","title":"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis","version":1},"cited_work":{"arxiv_id":"2311.10798","doi":"10.48550/arxiv.2311.10798","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.10798","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lungren, Curtis P","venue":"arXiv (Cornell University)","work_id":"3b1f00de-79b6-4600-afbc-61182d9a7fef","year":2023},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"cited_paper":"/paper/2311.10798","citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:a7a98773ca5c9138edf3b1371270ae1176d5524d7635fe8b77e40c73de80b288","observation_id":"be1003cb-467a-41b6-8d3e-02ce079443fd","resolution":{"observed_at":"2026-06-28T19:22:34.682485Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Study of thoracic ct in covid-19: the stoic project,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:f4a9b49e40dbce64e5608d84e82d3d73a62ecb9aede1cf2da070bda320181953","observation_id":"54869d1c-263a-41a5-9ab3-d55793e1beca","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Development of a large-scale grounded vision language dataset for chest ct analysis,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:c77f7cbdf702ead8e42350d327daece4ae988f44206974b82dfc978062d74c82","observation_id":"66b17409-a861-4896-a55a-947f69500eab","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"The rsna pulmonary embolism ct dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:a07db337cb75be31470e24f6109d7ae1a0cfcffc90dcf35004db6c5a43818a6c","observation_id":"c47b66bc-6c38-4589-90e7-34342083f384","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:285388c584bba38f890f4bd0d70f21290141019c405ed132d3a6f11be1ac4963","observation_id":"169fef0f-6fbe-4834-93fa-79c5a0982186","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T19:16:42.139096Z","title":"Towards data-efficient learning: A benchmark for covid-19 ct lung and infection segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-06-28T19:16:42.139096Z"},"links":{"citing_paper":"/paper/2606.00602"},"observation_digest":"sha256:a8a5b5b7be69ff7edc3f5d962cd57328f3960b24320a151e5af706c87cf5bc37","observation_id":"aa74c36a-fb1c-4fe6-8bfe-33166bcca46e","resolution":{"observed_at":"2026-06-28T19:16:42.139096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.00602","last_updated":"2026-05-30T07:59:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T18:26:22.873680Z","submitted_at":"2026-05-30T07:59:21Z","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":94,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":110},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 100 of 110 outbound references and 0 inbound Pith citation observations for arXiv:2606.00602."}