{"as_of":"2026-08-10T17:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a39c28f9d0f133ed22a089a73167e3e5cbc7befc10417ab346ba38340e457a6b","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:36:03.482002Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T17:45:36.546223Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T06:11:03.132823Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"cited_work":{"arxiv_id":"2507.02488","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.02488","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Medformer: Hierarchical medical vision transformer with content-aware dual sparse selection attention.arXiv preprint arXiv:2507.02488, 07 2025","venue":null,"work_id":"dc6af36d-9f31-425f-92d1-432c05aabaae","year":2025},"citing_paper":{"arxiv_id":"2604.08868","last_updated":"2026-04-10T02:03:25Z","snapshot_observed_at":"2026-08-08T14:01:05.825833Z","submitted_at":"2026-04-10T02:03:25Z","title":"MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T17:45:36.546223Z"},"links":{"cited_paper":"/paper/2507.02488","citing_paper":"/paper/2604.08868"},"observation_digest":"sha256:64886e6b939cb4893f48e201de7c7f1a1b29adbfd3f95c347cb10218db694950","observation_id":"2d4ea642-226e-494d-8238-90e5f548f606","resolution":{"observed_at":"2026-05-11T06:11:03.137458Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.02488/citation-record","integrity":"/paper/2507.02488/integrity","json":"/paper/2507.02488/citation-record.json","paper":"/paper/2507.02488"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.795863Z","title":"Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics,","venue":null,"work_id":"724b6c80-a3d2-468c-8082-213e1680c32d","year":2024},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.277857Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:b1cba9b2245957c0b1eb1c0dcb01ccc9cef702473b57ef1d4d9e782802ce8142","observation_id":"ed241459-bb4b-4522-9db3-2e48d6a55381","resolution":{"observed_at":"2026-08-06T20:36:08.868752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.708038Z","title":"Improved prediction model of protein and peptide toxicity by integrating channel attention into a convolutional neural network and gated recurrent units,","venue":null,"work_id":"2da1eb3c-514e-4998-9acd-5d62a1cad68b","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.369304Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:04af78dcdebb8403bc35f58b6fce95996b5a14e83497b153e8f3b4ee87292c0d","observation_id":"7768d1c9-8953-42b6-90ce-1fdbdf639804","resolution":{"observed_at":"2026-08-06T20:36:08.753171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:35:59.440749Z","title":"Transformers in medical imaging: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.440749Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:319d006a3248ce32a758b85c12ef2f83878bdebf36f132e6a2137af632b45923","observation_id":"1e5b4a61-7c67-42ab-bc27-a4bbbedf7eca","resolution":{"observed_at":"2026-08-06T20:35:59.440749Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.554010Z","title":"Advances in medical image analysis with vision transformers: A comprehensive review,","venue":null,"work_id":"60aebe33-9d2e-4e1b-adcc-b49a77d541b8","year":2024},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.534447Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:968d329e160b7756f3f51a75eb1f8dcb41c5c0c4378629baa2ef26fdabc25344","observation_id":"4fea8696-2a14-4f0e-be5c-fd901cf374fb","resolution":{"observed_at":"2026-08-06T20:36:08.611268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.09038","last_updated":"2021-08-20T08:01:19Z","snapshot_observed_at":"2026-08-04T16:19:37.799838Z","submitted_at":"2021-08-20T08:01:19Z","title":"Is it Time to Replace CNNs with Transformers for Medical Images?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.09038","snapshot_observed_at":"2026-08-06T20:35:59.600970Z","title":"Is it time to replace cnns with transformers for medical images?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.600970Z"},"links":{"cited_paper":"/paper/2108.09038","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:8341f2d043e981aff67e4dc526755fea7e3d472cb79ecd53daab2f8753afda8b","observation_id":"bab5ee4f-8b51-4746-a407-9698e93df481","resolution":{"observed_at":"2026-08-06T20:35:59.600970Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.402773Z","title":"Lesion-aware transformers for diabetic retinopathy grading,","venue":null,"work_id":"7ef8a2c7-4457-4fef-8808-0e032a013ad7","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.717830Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:7e93c641741e23fac3f20b356398868d54c6087731df91e359e6e76703223516","observation_id":"503d779b-ccec-4f81-9653-7598ff1db1ee","resolution":{"observed_at":"2026-08-06T20:36:08.467198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.254089Z","title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification,","venue":null,"work_id":"9ddc0d03-538b-48d8-8b04-166ff0a92d33","year":2018},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.827393Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:a31deb5d7d5c1a89ac040146bcb43ec13f3a7ebf7d3fcc8e06305a42078e84ae","observation_id":"920a3166-b613-43af-b989-3cfa197d2fec","resolution":{"observed_at":"2026-08-06T20:36:08.327007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:08.114543Z","title":"A graph-transformer for whole slide image classification,","venue":null,"work_id":"67f7f11d-330e-44dd-adaa-0eb9914e4dfc","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:35:59.931227Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:3a541581ad97e24ffcf11ce60eb9b57fb71b809274e25a0f361bfec66c561c4c","observation_id":"afd2ac4b-ca6e-46dc-b2c9-18a70010c2f2","resolution":{"observed_at":"2026-08-06T20:36:08.185460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-08-10T02:39:10.770770Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-06T20:36:00.037470Z","title":"Transunet: Transformers make strong encoders for medical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.037470Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:92805de7740dc028bd65d814afdae7663f64461733917d3ab08234f3a9bff94a","observation_id":"325c5129-f318-4035-a6e7-7b3b274cb2b3","resolution":{"observed_at":"2026-08-06T20:36:00.037470Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.970402Z","title":"Transfuse: Fusing transformers and cnns for medical image segmentation","venue":null,"work_id":"b8e23d87-093c-4b74-9ed4-d72ba4fa3caa","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.135867Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:c06eb6f2c8506b619a4d021e9ae4971cc5876f5555320b7555aa07dadb198c05","observation_id":"96a0c115-90af-4755-a91b-c797708aa080","resolution":{"observed_at":"2026-08-06T20:36:08.023132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:00.231610Z","title":"Swin-unet: Unet-like pure transformer for medical image segmenta- tion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.231610Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:bfadd110d0f5f0eba3ac53c42f0238e512cd8d333b8060db9d70cd1a5ac9f87c","observation_id":"2f59280e-3b1d-476b-a11d-31df71a23823","resolution":{"observed_at":"2026-08-06T20:36:00.231610Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.823033Z","title":"Pubic symphysis-fetal head segmentation network using biformer attention mechanism and multipath dilated convolution,","venue":null,"work_id":"b087a5ef-8973-4411-be0f-66e0e494a665","year":2025},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.332482Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:2ad9a87adda8aae0d83e653e7f6e473a10073d7396d79c68c3ad109eedc3bae9","observation_id":"e86ff700-092a-4464-a4d9-93dc6ab1c261","resolution":{"observed_at":"2026-08-06T20:36:07.876056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00722","last_updated":"2024-09-30T04:23:54Z","snapshot_observed_at":"2026-08-10T10:53:03.939645Z","submitted_at":"2024-01-01T10:49:09Z","title":"BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00722","snapshot_observed_at":"2026-08-06T20:36:00.434728Z","title":"Brau-net++: U-shaped hybrid cnn-transformer network for medical image segmenta- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.434728Z"},"links":{"cited_paper":"/paper/2401.00722","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:c8ae9501e5658e59ee1cd71c0833e5ac95befdb5d17ce135198db3a61814fc0e","observation_id":"a35a9d08-00e8-43dd-9d07-6abc86cf2ea1","resolution":{"observed_at":"2026-08-06T20:36:00.434728Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.690106Z","title":"Spine-transformers: Vertebra detection and local- ization in arbitrary field-of-view spine ct with transformers","venue":null,"work_id":"4d080016-f028-4c8f-954b-ef34cce3e73d","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.498184Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:a935d4c6392557e7b4d93c65083ebb35500f1339d95a90e27e04aa92242b2be6","observation_id":"5a4b0492-d144-4f5d-aaac-19ddc797ab1f","resolution":{"observed_at":"2026-08-06T20:36:07.754137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.572547Z","title":"Multi-view vertebra localization and identification from ct images,","venue":null,"work_id":"fb1fb187-0a51-4c2c-a529-53cb82e61ff3","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.569402Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:fd567c7dd0a93d5e967484590691011d095c77daac9a4db6a37a79e4ec7a0085","observation_id":"70a358e8-7b93-4681-ab38-a4169405e72a","resolution":{"observed_at":"2026-08-06T20:36:07.628952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T20:36:00.617988Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.617988Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:e09be57a5a3ce658983cb19ba6924ecae49bd40f789147499e71934a86d6a1e1","observation_id":"124db99e-f85f-4649-bafc-6aabe7b11d44","resolution":{"observed_at":"2026-08-06T20:36:00.617988Z","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":"2026-08-06T20:36:00.689607Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.689607Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:3b30b61fe06c5f12459993b00720ea93194213cec455de8821ca9eb6494a3983","observation_id":"f5bf91df-aed2-4a7d-8ac8-e5a42a5c6580","resolution":{"observed_at":"2026-08-06T20:36:00.689607Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.421564Z","title":"Biformer: Vision transformer with bi-level routing attention,","venue":null,"work_id":"98043ad0-cd62-4c5d-8485-d804d45e0f7e","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.754662Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:818c9449c6f3f750145f990be12711fa5e160f447f3d46981579805e05828cc0","observation_id":"c720b681-4a61-415d-8421-52d4da0eb221","resolution":{"observed_at":"2026-08-06T20:36:07.491042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:00.820255Z","title":"Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.820255Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:92a27b69ecb90a307673920ce04660946b0444fe66bf83bb3cecc45b8d96d87a","observation_id":"d8366212-2e18-463e-b2d0-59a0e1a9e45d","resolution":{"observed_at":"2026-08-06T20:36:00.820255Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.289231Z","title":"Medical transformer: Gated axial-attention for medical image segmentation","venue":null,"work_id":"87d7fb13-ca4a-4cbf-851e-d03948ccf040","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:00.927842Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:070f75fc2193542c93eebd9b7f0b82e4de841dea562fdf344dfb2e49dbe514cc","observation_id":"9a64cf3a-d3ed-4fb6-bfe2-4fb8a00af31e","resolution":{"observed_at":"2026-08-06T20:36:07.363580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:01.014435Z","title":"Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.014435Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:48be6e4e60e80c9053da60eac0d999f0d6d2dbeccb9646b6c072be5fabf02067","observation_id":"5ca5824a-0968-4dc4-b6a2-d6aa97f525fc","resolution":{"observed_at":"2026-08-06T20:36:01.014435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.11637","last_updated":"2019-12-25T10:59:31Z","snapshot_observed_at":"2026-07-06T08:46:56.770342Z","submitted_at":"2019-12-25T10:59:31Z","title":"Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.11637","snapshot_observed_at":"2026-08-06T20:36:01.086622Z","title":"Explicit sparse transformer: Concentrated attention through explicit selection,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.086622Z"},"links":{"cited_paper":"/paper/1912.11637","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:7f2ff0b2c27e6bb1e9fd573162cd2a49373cc2b77e61497cb03bb89e3e95a4f3","observation_id":"c383f387-6eda-4c68-bb45-d2529daf5f92","resolution":{"observed_at":"2026-08-06T20:36:01.086622Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:07.149321Z","title":"Mil-vt: Multiple instance learning enhanced vision transformer for fundus image classification","venue":null,"work_id":"76aeca8d-07a6-40e1-92f9-0b931ad1c745","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.166085Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:aeb17f13246ff3e2f1355b207205c23ee7ece10f8265d370edeb5e6349558fc9","observation_id":"919ff204-36c3-4931-93d6-5d9347460cc4","resolution":{"observed_at":"2026-08-06T20:36:07.179695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.887687Z","title":"Self-ensembling vision transformer (sevit) for robust medical image classification","venue":null,"work_id":"ebcd2164-522e-40d8-996f-0bfb2426d943","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.237643Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:9fb7bebcc9eee528081bbeb9466ff437cc471ebb29d66033d6eb65390c704c1d","observation_id":"fde28095-c9b1-41fd-8278-17c3b84bbcbe","resolution":{"observed_at":"2026-08-06T20:36:06.997796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.800368Z","title":"Boundary- aware transformers for skin lesion segmentation","venue":null,"work_id":"e3e50bef-c218-4348-aeeb-c1977c8baf39","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.305543Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:6ec7dc6b96888ac0183e2ca2dc16fdced04058b5cced4cba12c73b787b892d3a","observation_id":"515a8fff-9909-487a-b7d1-95b4c50f29ab","resolution":{"observed_at":"2026-08-06T20:36:06.839518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.691898Z","title":"Transformer network for significant stenosis detection in ccta of coronary arteries,","venue":null,"work_id":"23acd8a7-0815-4b27-bdf2-d66673fb5ffa","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.395336Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:0d2655e9916643628441f6b6336e33e76a33adf6dc3a18ce6cf8290bf2aa2588","observation_id":"321b640a-ba17-4652-b689-5f1d5b92fe53","resolution":{"observed_at":"2026-08-06T20:36:06.745443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.582664Z","title":"Ida-net: Inheritable deformable attention network of structural mri for alzheimer’s disease diagnosis,","venue":null,"work_id":"b7103f16-412e-44b3-85c0-b652c5eb4391","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.476179Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:9bfa7881661a324d028e58731d4e38d3dd5b1740551ddea5d63b015676cff49f","observation_id":"b5f49055-8232-4bf0-8e52-96248de4537d","resolution":{"observed_at":"2026-08-06T20:36:06.639496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:01.556283Z","title":"Vision transformer with deformable attention,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.556283Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:460027fe440e163c9c824a9956a0807affe032b92e73e367f994cfb3252a77f9","observation_id":"9463ddf1-053e-49bf-9c97-cc57cb9bbc80","resolution":{"observed_at":"2026-08-06T20:36:01.556283Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.313369Z","title":"Hifuse: Hierarchical multi-scale feature fusion network for medical image classification,","venue":null,"work_id":"07f89e44-229c-45bc-8b8d-b9f6c0f3f2b1","year":2024},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.631442Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:b0dd19f873343e7bf3fd484405243517e3f7794380eb0619bc3f2a54453fc6aa","observation_id":"f7b075ea-fa0f-4ad1-9dc4-2b5bcb1d4517","resolution":{"observed_at":"2026-08-06T20:36:06.444842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:06.052622Z","title":"Pocformer: A lightweight transformer architecture for detection of covid-19 using point of care ultrasound,","venue":null,"work_id":"5b679cdc-c840-4483-9e26-b371c452334d","year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.699851Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:13b1869483b6bd3b7e3fddc30f0dfa3be19924112ae57c26acd71e37d6e6c0dd","observation_id":"e2eeda3b-3f88-468d-97fc-62b3a8ba55de","resolution":{"observed_at":"2026-08-06T20:36:06.144619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04768","last_updated":"2020-06-14T08:15:54Z","snapshot_observed_at":"2026-07-06T09:27:03.809621Z","submitted_at":"2020-06-08T17:37:52Z","title":"Linformer: Self-Attention with Linear Complexity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04768","snapshot_observed_at":"2026-08-06T20:36:01.755569Z","title":"Linformer: Self-attention with linear complexity,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.755569Z"},"links":{"cited_paper":"/paper/2006.04768","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:9569f07d4fdf309312e7e6aac8fc5072a16b2cdc12b2d0e551119e6835caa051","observation_id":"adbd8c90-95d0-4f77-9339-5ddfc4c12975","resolution":{"observed_at":"2026-08-06T20:36:01.755569Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.914273Z","title":"Kernel attention transformer for histopathology whole slide image analysis and assistant cancer diagnosis,","venue":null,"work_id":"391ee8ed-bbc9-415d-ad41-4d49bc31cffc","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.821310Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:ec98662c2bc524700fa197b90e2e9c815d2dbe88d1058c49acd0a66aefe66dc7","observation_id":"59b4b769-1039-483a-a035-3052b8696abe","resolution":{"observed_at":"2026-08-06T20:36:05.961900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.794417Z","title":"Mg-trans: Multi-scale graph transformer with information bottleneck for whole slide image classification,","venue":null,"work_id":"f4c56500-9e8c-4d7a-92d7-139464c372ba","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.877927Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:f7be3f33471318fb4e0e28bcd7d09a006ce70c5fb7972bc677cc3641567ae641","observation_id":"8a69cd3b-4e9d-4e25-8424-032e6c1c79ba","resolution":{"observed_at":"2026-08-06T20:36:05.831201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.604612Z","title":"Msaanet: Multi-scale axial attention network for medical image segmentation,","venue":null,"work_id":"27b3b848-e0e8-4f45-945b-769f08f5eac8","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:01.959957Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:36111772230f2ddeb04197e83975f3e6bcb3c0f1ccac0c5dc8e8e095cbf04ffc","observation_id":"a8bb732a-171d-4422-888a-aa2715203ed2","resolution":{"observed_at":"2026-08-06T20:36:05.703438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.443860Z","title":"Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,","venue":null,"work_id":"ad43ffb3-5643-4a4e-8566-11cbde639b74","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.031763Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:cdabc2003f0f3ffed862548ba77a15b4ede19aaac30f2a5905937e5fddcce941","observation_id":"5d73d453-b9fc-4aab-8310-37f0541e3fef","resolution":{"observed_at":"2026-08-06T20:36:05.506768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.286770Z","title":"Maxformer: Enhanced transformer for medical image segmentation with multi- attention and multi-scale features fusion,","venue":null,"work_id":"7be50490-ec8f-41d9-afb0-012daa825ba3","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.149128Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:431e559c8cb4f942e7dd97d6cb6af888bce2225f2b8a44654811f67ef75d77fd","observation_id":"9200b884-5662-4f68-8249-900b59d2f628","resolution":{"observed_at":"2026-08-06T20:36:05.369912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:05.119119Z","title":"Missformer: An effective transformer for 2d medical image segmentation,","venue":null,"work_id":"6dcbc39e-3266-41e1-baad-6c39205fae2f","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.215222Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:e39d36333438a83886156d713c0f92ec0709d972af09acd68bf79f0020402cc1","observation_id":"b3aefd4b-b2d1-4c30-9edd-71c331f47648","resolution":{"observed_at":"2026-08-06T20:36:05.218429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:02.305495Z","title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.305495Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:33e8be7c96ffb976d95d388fe1cc3abbab29ce7c297fca56c535d7f935783d9b","observation_id":"bfba0571-7fbf-4a2d-9da2-a2beff41b653","resolution":{"observed_at":"2026-08-06T20:36:02.305495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03368","last_updated":"2019-03-29T17:36:27Z","snapshot_observed_at":"2026-08-07T00:39:52.073704Z","submitted_at":"2019-02-09T04:18:10Z","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03368","snapshot_observed_at":"2026-08-06T20:36:02.393990Z","title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic),","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.393990Z"},"links":{"cited_paper":"/paper/1902.03368","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:39b484b632143a13e6e6f61ab452e4795311b368a95a42582b98b791c6665670","observation_id":"145412ff-9471-4e8d-a309-f50c366b9f4f","resolution":{"observed_at":"2026-08-06T20:36:02.393990Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:04.898236Z","title":"A real-world dataset and benchmark for foundation model adaptation in medical image classification,","venue":null,"work_id":"3d91d5da-0758-45a0-9e7b-88026c1d5152","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.483270Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:7f767e3af593763c1d6f423d51ef9f5be06b07498dca7a16545b0d85452965d0","observation_id":"f981aa95-e76d-47e0-b670-91a5e1af884e","resolution":{"observed_at":"2026-08-06T20:36:04.989417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:02.578795Z","title":"Brain tumor mri dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.578795Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:60fdc1580323e18f5ade7836cbe293e19bf707d5118772cac864f99bc2c0c501","observation_id":"29063e16-6b58-42a3-a1d2-d6275f7f56db","resolution":{"observed_at":"2026-08-06T20:36:02.578795Z","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":"2026-08-06T20:36:02.683727Z","title":"Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.683727Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:d16893f17a9a5c9d2094ca0bc45202254e4ee890484756d1ae4cd4cd69da4b27","observation_id":"2902eea3-74a0-4def-9ed5-05f02ffbca31","resolution":{"observed_at":"2026-08-06T20:36:02.683727Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:04.717718Z","title":"Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge (2015),","venue":null,"work_id":"552071db-6b6b-4482-81aa-79dd2711d40e","year":2015},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.762465Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:a285d4b75bc16aec4997fab63ee32be5904347284e19d03330af39b59e74a158","observation_id":"f56f6343-6414-472d-9eb0-6f1ef663750d","resolution":{"observed_at":"2026-08-06T20:36:04.789959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:04.557472Z","title":"Kvasir-seg: A segmented polyp dataset,","venue":null,"work_id":"967f585b-9dc5-42fa-b3df-2defed5c0de0","year":2020},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.876780Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:9d6c26a4cdd09ca3c495538727c7eb1665d2f358cbd029b70b84fdcfe5f68751","observation_id":"9bd89ee0-ce6e-4b45-8760-ddd0d236b9de","resolution":{"observed_at":"2026-08-06T20:36:04.629016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:04.307787Z","title":"Brain tumor detection dataset,","venue":null,"work_id":"e3f4d090-7816-409c-846c-3012d64f9794","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:02.980462Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:c72a5e3799e7e1fa121cab0b514b59b0c86a7e72c1ef4797282a67fd55f2e37c","observation_id":"fddff2e2-d30c-40be-a3d8-6273e1f80933","resolution":{"observed_at":"2026-08-06T20:36:04.456424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09263","last_updated":"2022-12-19T06:17:22Z","snapshot_observed_at":"2026-08-09T06:12:54.462182Z","submitted_at":"2022-12-19T06:17:22Z","title":"Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2212.09263","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.09263","snapshot_observed_at":"2026-08-06T20:36:03.632156Z","title":"Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation","venue":"eess.IV","work_id":"4081ea4b-558e-4a4c-82c8-e0bcaed97d53","year":2022},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.045785Z"},"links":{"cited_paper":"/paper/2212.09263","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:85294653709398d4e574b87d69be9edf60b41f8d226738986b3b39758115bc44","observation_id":"607c3e0d-9859-4d13-861d-0476ba0da976","resolution":{"observed_at":"2026-08-06T20:36:03.753395Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:04.143370Z","title":"Medical image segmentation via cascaded attention decoding,","venue":null,"work_id":"0e0f6718-210a-477a-8ce0-8301e6b23d75","year":2023},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.111242Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:d408974052a3fc7964f43f034d3d0997e6dc176e22eed27928f2591a989df9e3","observation_id":"b63d7afd-983d-47e6-85e0-4391ab4fd8b9","resolution":{"observed_at":"2026-08-06T20:36:04.226901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T20:36:03.208759Z","title":"Focal loss for dense object detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.208759Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:734f2ff18b05f8ca9a53db5d9afbf9963b961d1d4b7f2fae4f37751d5d8fd5f4","observation_id":"02ef43e2-947e-4e77-b805-609954f6aac9","resolution":{"observed_at":"2026-08-06T20:36:03.208759Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:36:03.976105Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks,","venue":null,"work_id":"c0a17f09-684d-44d6-b920-64dfc063d678","year":2016},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.289634Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:0bacd0c76eccb493113ad319a5dc52ca1579c4d75f3df9cc5113b0fad017823f","observation_id":"347fa4a2-e777-404f-ab0c-936e244b9e97","resolution":{"observed_at":"2026-08-06T20:36:04.055442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04159","last_updated":"2021-03-18T03:14:26Z","snapshot_observed_at":"2026-07-06T10:02:45.105181Z","submitted_at":"2020-10-08T17:59:21Z","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04159","snapshot_observed_at":"2026-08-06T20:36:03.380923Z","title":"Deformable detr: Deformable transformers for end-to-end object detection,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.380923Z"},"links":{"cited_paper":"/paper/2010.04159","citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:aa8ab9fa9865593c055d258228df5958f885f43f0b6e4f4499ce8f3b58b5434d","observation_id":"e7e49aa0-384e-4288-88b9-c435d9ee48a0","resolution":{"observed_at":"2026-08-06T20:36:03.380923Z","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":"2026-08-06T20:36:03.482002Z","title":"Unified perceptual parsing for scene understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T20:36:03.482002Z"},"links":{"citing_paper":"/paper/2507.02488"},"observation_digest":"sha256:161c7e17543e70d224a9d9275ce188170dfbe09139b708019fd5d057178c8f4e","observation_id":"756f9c74-f8c4-437d-a3df-6fbd3cd79e74","resolution":{"observed_at":"2026-08-06T20:36:03.482002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.02488","last_updated":"2025-08-05T03:35:06Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T17:26:36.540495Z","submitted_at":"2025-07-03T09:51:45Z","title":"MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attention"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":31},"total_outbound_references":51},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2507.02488."}