{"as_of":"2026-08-21T05:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6eb02dcbbb2653d8f6f8cfce13ab09813d969696bfa7778e26c09dbdfcbbfcf","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:37:03.145694Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"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/2411.16123/citation-record","integrity":"/paper/2411.16123/integrity","json":"/paper/2411.16123/citation-record.json","paper":"/paper/2411.16123"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.456610Z","title":"Role of segmentation in medical imaging: A compara- tive study","venue":null,"work_id":"67bbb51a-f185-4476-a5d3-1b9ce2bb0557","year":2011},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.726280Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:fd435602aa73c2c558613523aee115e72af7bd0f0fd544fbe5c9f5f9b40a9a4d","observation_id":"0d522537-8d99-404d-8ea4-48d2495c7baa","resolution":{"observed_at":"2026-08-12T13:37:04.462165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2407.07042","last_updated":"2024-07-18T07:58:11Z","snapshot_observed_at":"2026-08-16T13:35:42.447755Z","submitted_at":"2024-07-09T17:04:08Z","title":"ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07042","snapshot_observed_at":"2026-08-12T13:37:02.731123Z","title":"Protosam-one shot medical image segmentation with foun- dational models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.731123Z"},"links":{"cited_paper":"/paper/2407.07042","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:1479d9bf56c0e1811094e7cc26f84515cca3517cfd50b989a96880774a76513f","observation_id":"ad669645-7da0-4357-8e0d-0410147a85ff","resolution":{"observed_at":"2026-08-12T13:37:02.731123Z","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":"2026-08-12T13:37:04.439868Z","title":"V oxelmorph: a learning framework for deformable medical image registration","venue":null,"work_id":"8b1d25fa-8e48-4088-aafa-c9c61ce654d3","year":2019},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.736339Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:7c4992276cff65754431939b744a8921351ee00191b4df3251dad1044f7ba726","observation_id":"8aea59ae-7ab0-469d-8634-4c85b67292dd","resolution":{"observed_at":"2026-08-12T13:37:04.444602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.421607Z","title":"Visual prompting via image inpaint- ing","venue":null,"work_id":"782b6570-b155-4fdc-86bf-d2ad71a7ddf3","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.740363Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:7dee043972c6b76c6705b556b390c4be8450b95a96890d678a4174847dc7d6cb","observation_id":"0fb26121-0bae-45c2-bee6-f057d3f7136c","resolution":{"observed_at":"2026-08-12T13:37:04.428039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.399098Z","title":null,"venue":null,"work_id":"ef1db8c8-f8ba-4046-ab3e-9ba6b5385a1a","year":2000},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.745662Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b93660655b5bd40daba944225b3498f238426567ca757e7287afe6bdb6d1e0a2","observation_id":"a2472ff9-26ce-41a6-89f1-6f54d7affeb2","resolution":{"observed_at":"2026-08-12T13:37:04.406187Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T13:37:02.750732Z","title":"Lan- guage models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.750732Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:a3453aea55fe2c62d87c17837e8e6ca3cf7c8fbfd4cb40cee704e08074f03125","observation_id":"572b2958-1975-42e0-b676-9eb56999a36d","resolution":{"observed_at":"2026-08-12T13:37:02.750732Z","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":"2026-08-12T13:37:02.756421Z","title":"Lan- guage models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.756421Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:bb700292fc7d0d63d1603a5d2dd10445a095e3ca2aa9657974897f3b3653647f","observation_id":"9bc4927b-b503-4944-8cc5-46d4178baa70","resolution":{"observed_at":"2026-08-12T13:37:02.756421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.06131","last_updated":"2023-04-12T19:36:46Z","snapshot_observed_at":"2026-08-18T17:56:59.170846Z","submitted_at":"2023-04-12T19:36:46Z","title":"UniverSeg: Universal Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.06131","snapshot_observed_at":"2026-08-12T13:37:02.760984Z","title":"Uni- verseg: Universal medical image segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.760984Z"},"links":{"cited_paper":"/paper/2304.06131","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b8766a6dd18e74820700b9a117c6f64479ae894885f3601835946cdb21392c56","observation_id":"b382b340-0762-4e65-ae18-a89a1f71a9f4","resolution":{"observed_at":"2026-08-12T13:37:02.760984Z","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":"2026-08-12T13:37:04.360874Z","title":"Semi-supervised task-driven data augmentation for medical image segmentation","venue":null,"work_id":"625aae21-49ff-4097-8893-27eed29c30c3","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.768245Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:244a96518353ac2306545106a02222667d05306c75a98ae26ddca86ec9544eea","observation_id":"f47b7e44-8dcc-48aa-9298-53d83171760c","resolution":{"observed_at":"2026-08-12T13:37:04.365821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.342977Z","title":"Remedios, Shunxing Bao, Bennett A","venue":null,"work_id":"82ce78e7-7bbc-4be2-9f43-39b772ba3694","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.773507Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:3f6dcc6dddf59fba0569ae7dc92c7da3b4e2a71069ce327e5c26a88f4e9871e2","observation_id":"47ac2de2-74ae-4754-ac7f-73d3233397bb","resolution":{"observed_at":"2026-08-12T13:37:04.348902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-12T13:37:02.777728Z","title":"Measures of the amount of ecologic association between species","venue":null,"work_id":null,"year":1945},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.777728Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:391ef94d551c46d0380df1fc3f370e1063d59d41026d1607af94e37b0ac50785","observation_id":"c34da6c9-19d4-4b6b-bc94-4cad7f0dae36","resolution":{"observed_at":"2026-08-12T13:37:02.777728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00234","last_updated":"2024-10-05T11:47:02Z","snapshot_observed_at":"2026-07-06T14:36:25.690733Z","submitted_at":"2022-12-31T15:57:09Z","title":"A Survey on In-context Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00234","snapshot_observed_at":"2026-08-12T13:37:02.781757Z","title":"A survey for in-context learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.781757Z"},"links":{"cited_paper":"/paper/2301.00234","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:08c1f5561a607b17999dc1185c2458b8903274e5ef05f18f615a4d821c138828","observation_id":"58e42223-5f80-4c93-ae21-9c6993cc6235","resolution":{"observed_at":"2026-08-12T13:37:02.781757Z","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":"2026-08-12T13:37:04.304670Z","title":"Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis","venue":null,"work_id":"d50ac820-62e7-42e3-808b-736101d560ff","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.786583Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:cd30da1e96620925833c10aeb488bdcefaacea94ad35f080c1b8df29ed8fe350","observation_id":"30402e6f-0d40-4586-b56d-cc846b4d6fb8","resolution":{"observed_at":"2026-08-12T13:37:04.314394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2104.08821","last_updated":"2022-05-18T12:29:49Z","snapshot_observed_at":"2026-07-06T11:01:05.577957Z","submitted_at":"2021-04-18T11:27:08Z","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08821","snapshot_observed_at":"2026-08-12T13:37:02.792578Z","title":"Simcse: Simple contrastive learning of sentence embeddings","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.792578Z"},"links":{"cited_paper":"/paper/2104.08821","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:e47583df3e675f3ee93f4c0a64828f7a4cd09ff5f038c1186b79f5693533bf14","observation_id":"6eeb347f-fd29-4363-9733-5a65e6dfb1a5","resolution":{"observed_at":"2026-08-12T13:37:02.792578Z","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":"2026-08-12T13:37:04.287917Z","title":"Variational encoding and decoding for hybrid supervision of registration network","venue":null,"work_id":"67dbffab-a34e-4e34-900b-9baf81b83ccb","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.797835Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:61b7b32724e23b864f39f752f47181b890cbcb64f34f8fd23d236d41652953ff","observation_id":"41a30263-f5ad-4022-a1db-aab5237ebcb5","resolution":{"observed_at":"2026-08-12T13:37:04.293430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.270766Z","title":"Domain adaptation for medical image analysis: A survey","venue":null,"work_id":"69fed593-619b-45b7-9978-2c60ae8b3224","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.802918Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:98edb3daec4565868748acd6a9fa1566d4560b011da38f2d89e73fe4d35e3dc3","observation_id":"6bc8b139-3aaf-4e39-92f2-10038bfcbdae","resolution":{"observed_at":"2026-08-12T13:37:04.276550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.255861Z","title":"Ellen Grant, and Yangming Ou","venue":null,"work_id":"1711bc65-e485-4f2d-9747-bed15669fc0d","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.807626Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:4bb879f82cc783608575e42030de3a2ef9fd1393f59cbf9eb0849feebfe4fac9","observation_id":"7a44ea71-1f26-495c-b93a-1b87bc6a5777","resolution":{"observed_at":"2026-08-12T13:37:04.260545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.240942Z","title":"Learn2reg: comprehensive multi-task medical image regis- tration challenge, dataset and evaluation in the era of deep learning","venue":null,"work_id":"e09f1ac5-3812-4e2a-9ea0-8be41fd983c7","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.813486Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:a5e01f9510cb50e290b5efe502a6355a5b1497e1f2aced1d28dc17b5a53c1510","observation_id":"827e6547-60a6-4294-ab2c-de9aa2c8d13e","resolution":{"observed_at":"2026-08-12T13:37:04.246155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.221701Z","title":"When sam meets medical images: An investigation of seg- ment anything model (sam) on multi-phase liver tumor seg- mentation, 2023","venue":null,"work_id":"1ec1f469-6fd5-4b77-8a1e-830cf1e39dd9","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.820067Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:3ecf764a8df3e3e6f315f057831b8fe162f08f0ab5ec536295471cec1d5b4d22","observation_id":"85b62b25-0e86-494b-938b-9d32afec6b46","resolution":{"observed_at":"2026-08-12T13:37:04.226563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-12T13:37:02.826793Z","title":"Many-to-many splatting for efficient video frame interpola- tion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.826793Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:fb16a4492432e23b4b7cabf9824ce895440b9e914dca6e5c40c22803614952ca","observation_id":"b1b93888-7b8f-459b-bfe9-bf947c1d33ac","resolution":{"observed_at":"2026-08-12T13:37:02.826793Z","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":"2026-08-12T13:37:04.195413Z","title":"Two public chest x-ray datasets for computer-aided screening of pulmonary diseases","venue":null,"work_id":"f8db1fe8-0f2d-4512-a640-d44266472fa7","year":2014},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.833489Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:e5784db518a9a4fba4a2ee2acb5a854edece4993600bb3137f9254287d80ba54","observation_id":"a1d35474-40c3-450f-9ec2-40bb03854a95","resolution":{"observed_at":"2026-08-12T13:37:04.200465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2409.11910","last_updated":"2024-09-18T12:11:59Z","snapshot_observed_at":"2026-08-18T18:23:49.036381Z","submitted_at":"2024-09-18T12:11:59Z","title":"Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer","version":1},"cited_work":{"arxiv_id":"2409.11910","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.11910","snapshot_observed_at":"2026-08-12T13:37:03.488444Z","title":"Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer","venue":"eess.IV","work_id":"583ca5e5-82ef-44f7-8f12-83362922d8b0","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.839587Z"},"links":{"cited_paper":"/paper/2409.11910","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:1f7a0fe8c2eaccac2e1043a88c73eed5d7889ff681d8c9421845be71e411412d","observation_id":"abe0830c-2d7d-4d83-82c9-c56b94fa0700","resolution":{"observed_at":"2026-08-12T13:37:03.492957Z","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":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.169690Z","title":"On the effect of inter-observer variability for a re- liable estimation of uncertainty of medical image segmenta- tion","venue":null,"work_id":"e79fb61c-a80f-44ca-a9fd-f0174cd44e1a","year":2018},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.849706Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:7b36622fa5b304bc20f2a4df563abcf1d5a289845c8ce1ac064e6aae96415f12","observation_id":"0650a637-9a4e-4541-b9b9-795fe93794ac","resolution":{"observed_at":"2026-08-12T13:37:04.174766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2410.20294","last_updated":"2024-10-27T00:05:15Z","snapshot_observed_at":"2026-08-18T18:23:37.643407Z","submitted_at":"2024-10-27T00:05:15Z","title":"Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions","version":1},"cited_work":{"arxiv_id":"2410.20294","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.20294","snapshot_observed_at":"2026-08-12T13:37:03.465454Z","title":"Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions","venue":"cs.CV","work_id":"74a29add-4745-489b-bd89-b28b3347ac89","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.857531Z"},"links":{"cited_paper":"/paper/2410.20294","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:5b8414cd5d4ded88b9ca53818e05019fb2cca54d7e18f4efd77da19e1d223278","observation_id":"e83806e0-9911-40db-bf66-56522a38e135","resolution":{"observed_at":"2026-08-12T13:37:03.472945Z","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":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.151323Z","title":"Data-efficient unsupervised interpolation without any intermediate frame for 4d medical images","venue":null,"work_id":"686f5b4d-a043-4b79-a7ac-4ef5d427e0ac","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.866116Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:427d95421851434739ae1d1dbcfefdba073e2019f6d52e282c37ca0af3a063e9","observation_id":"ba2d5d90-d892-4f32-8855-0d5a6da843f5","resolution":{"observed_at":"2026-08-12T13:37:04.156781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-12T13:37:02.871856Z","title":"Segment any- thing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.871856Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:fd79e90d3c907250f34f60534e7b79579a9d557f3a2352c56d987ae18fb8de41","observation_id":"c89f4a8c-acf6-4c53-b2a1-9505e6e3df25","resolution":{"observed_at":"2026-08-12T13:37:02.871856Z","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":"2026-08-12T13:37:04.135447Z","title":"Buu-lspine: A thai open lumbar spine dataset for spondylolisthesis detection","venue":null,"work_id":"e6472851-2794-4a2c-8e57-e4849b60cb01","year":null},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.877868Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:c4fc7fb79b73eb6e07c835949bc0e77cde11b0a2fc8eefbb1f3dbff8b6b1a9c5","observation_id":"44e74f97-c4fc-49d9-87d0-54e2d2d26a35","resolution":{"observed_at":"2026-08-12T13:37:04.140532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.115095Z","title":"New index for cluster- ing tendency and its application to chemical problems","venue":null,"work_id":"6cf346e4-b384-43e6-9b48-e347bf02aa58","year":1990},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.882907Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:db0e221d81f938604e0a8c57aa409d9b83b4043367d12d921f35ad159fc6e7a8","observation_id":"69e3c75f-08ae-474b-ba4b-f3b852e1fbe3","resolution":{"observed_at":"2026-08-12T13:37:04.120905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.095952Z","title":"Deep learning for segmentation using an open large-scale dataset in 2d echocardiography","venue":null,"work_id":"45be226f-f05b-421a-be8a-22e96532cd20","year":2019},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.888803Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b48c2aa1ad9db3b99452a04fc63c758aaf26cdc3b29ca0107a551f1a2954eb2b","observation_id":"377dffac-bfe9-4c66-8580-2502a3c43b1b","resolution":{"observed_at":"2026-08-12T13:37:04.101378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2305.13310","last_updated":"2024-01-19T13:03:04Z","snapshot_observed_at":"2026-08-19T23:04:38.178579Z","submitted_at":"2023-05-22T17:59:43Z","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13310","snapshot_observed_at":"2026-08-12T13:37:02.894792Z","title":"Matcher: Segment anything with one shot using all-purpose feature matching","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.894792Z"},"links":{"cited_paper":"/paper/2305.13310","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:c6ac9288fb3ddb6710a530fb8dc251059d65706ea81ac3e2e444f0752159646f","observation_id":"ec5d69f2-c1c9-4403-be31-49853750e33f","resolution":{"observed_at":"2026-08-12T13:37:02.894792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-12T13:37:02.899401Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.899401Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:a3011d59ea3be8c75140ebb17ece7ba5ee6a405d36aa38313286ae14ceb1e145","observation_id":"cca99586-e99c-4f32-b840-b9004816f204","resolution":{"observed_at":"2026-08-12T13:37:02.899401Z","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":"2026-08-12T13:37:02.904231Z","title":"Segment anything in medical images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.904231Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:77a5cbe936b20e699065ee32ea944a385cfb8293f8c2ce8c4382d085e82345ef","observation_id":"78fab903-1263-4fca-86fe-f8b9fd2cfc68","resolution":{"observed_at":"2026-08-12T13:37:02.904231Z","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":"2026-08-12T13:37:04.063509Z","title":"Learn- ing deformable registration of medical images with anatom- ical constraints","venue":null,"work_id":"4c6e974e-3d02-49af-80aa-ad8d262bf9cd","year":2020},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.912262Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:65381ed578288a3b657dc9cc46d4c6ecfe29d7d3f4481db822fb400bbffa7243","observation_id":"ab786ff1-2cdc-4af8-888a-2a72984b1ea0","resolution":{"observed_at":"2026-08-12T13:37:04.069006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.045922Z","title":"Non-iterative coarse-to-fine transformer net- works for joint affine and deformable image registration","venue":null,"work_id":"213bb8a2-c3cf-463b-a86e-622d1bab90f5","year":null},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.917199Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:1c9147d99d51c919899363f523b15f1cea82a3bc26b7e396fb0428d117d96648","observation_id":"ea2598ac-a003-44b1-9106-fc7a831cd362","resolution":{"observed_at":"2026-08-12T13:37:04.050625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.027311Z","title":"Correlation-aware coarse-to-fine mlps for deformable medi- cal image registration","venue":null,"work_id":"dd75b096-7a3a-4da8-ae9b-c8a867e73a44","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.922589Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:0c2c7b4ed34a202ea2c8c505a7d955eae2ef5e0d98376eba551c9ea2fdbd1846","observation_id":"6407b2e0-7a1c-4126-9ce4-b0fd03518521","resolution":{"observed_at":"2026-08-12T13:37:04.034682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:04.002635Z","title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","venue":null,"work_id":"657654c2-8131-4819-96a6-9010512dcb01","year":2016},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.927147Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:5e019d19763c8d7f822b53341aca6b8a9b4ac18548564c628de13ea467625853","observation_id":"fb83e7a2-1062-4e70-9999-30d09674ebd0","resolution":{"observed_at":"2026-08-12T13:37:04.011892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.979337Z","title":"Fast binary dilation/erosion algorithm us- ing kernel subdivision","venue":null,"work_id":"a25c311f-eb77-432b-9650-09b4ec254bda","year":2006},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.932615Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:a8ab6ee489d3ee8e77ed489a0bb6ee02263472975386a0241563fc9bef6bc39d","observation_id":"994d43df-79fc-4eab-8b81-b9e6e371069d","resolution":{"observed_at":"2026-08-12T13:37:03.986651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.960097Z","title":"Context-aware synthesis for video frame interpolation","venue":null,"work_id":"a49776cb-59b3-4e32-aa17-d2d84e3980b6","year":2018},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.937696Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:aebeb8e333621a2c2b145d16526d2b63608813eb3209e9057ca6407e651528ea","observation_id":"d675d8b3-ffaa-441a-87e0-49070a17305b","resolution":{"observed_at":"2026-08-12T13:37:03.967063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T13:37:02.942720Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.942720Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:342cd65d180b67e0df5e85a1d812c87f33a5b0a64865d129bb296cba0c288279","observation_id":"e644e015-ff36-4ddc-9862-8981d5ec6e0c","resolution":{"observed_at":"2026-08-12T13:37:02.942720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-12T13:37:02.955256Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.955256Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:27aa70f8a681611208b75f6760e01b2ca9276ceb75bb4f20dfa6458af9c6578a","observation_id":"76ac5ec5-00f1-4242-93c4-d48490a03561","resolution":{"observed_at":"2026-08-12T13:37:02.955256Z","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":"2026-08-12T13:37:02.960224Z","title":"Video-based ai for beat-to-beat assessment of cardiac func- tion","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.960224Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:2079afe8eddf6dd0e9c33575369bc770045b0bf7702ccca5a6470a7c31cb2248","observation_id":"afcdf0ad-2d85-4196-93a2-3efecabaa903","resolution":{"observed_at":"2026-08-12T13:37:02.960224Z","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":"2026-08-12T13:37:03.895790Z","title":"Limitations of the ssim quality metric in the context of diagnostic imaging","venue":null,"work_id":"b6aa1c1a-003f-48f1-8c1e-76619996a967","year":2015},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.966485Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:ee0febd48476898be98aebd22b03f086e8e510b611384831c49c08f6956cfae9","observation_id":"ea7b2908-6c5e-489c-985d-4ee449f6064a","resolution":{"observed_at":"2026-08-12T13:37:03.902052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.877014Z","title":"Asymmetric bilateral motion estimation for video frame interpolation","venue":null,"work_id":"c39ee526-9b84-494a-ab3c-05e49e103aa2","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.971210Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b6f516e97ed89107781c161f4809779534924838afdca283b85e7c3d8ce06535","observation_id":"d93846dc-7b23-4f64-a3b2-2c51f03a411c","resolution":{"observed_at":"2026-08-12T13:37:03.883720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.848172Z","title":"Biformer: Learning bilateral motion estimation via bilateral trans- former for 4k video frame interpolation","venue":null,"work_id":"e6a2c268-9523-4bda-9c22-4209d121b699","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.976286Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:c55ebd0870f2d2ead2472ca382df5e17f5820d956dd2319f36812638cdf98d2b","observation_id":"c389c4c6-2daf-407e-90c8-e01fe3e76061","resolution":{"observed_at":"2026-08-12T13:37:03.858453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.831005Z","title":"Can we trust deep learning based diagnosis? the impact of domain shift in chest radiograph classification","venue":null,"work_id":"f359df53-7c8c-4cc4-803f-0a87eeadcd62","year":2020},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.982632Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:70cae013e9e20d3b2571a2db80f5a6624a15d5a7dcc5e5fbbb93178f1dd0dbfd","observation_id":"07e91582-c764-4bb3-9131-d76eca7c883d","resolution":{"observed_at":"2026-08-12T13:37:03.835529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-12T13:37:02.987957Z","title":"Improving language understanding by gen- erative pre-training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.987957Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b6e3ad56abf37940c9dd958a06b96eee42f1a4354214938b2e04c597bf360f4b","observation_id":"51ede80f-073a-459c-b31f-a6278479827e","resolution":{"observed_at":"2026-08-12T13:37:02.987957Z","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":"2026-08-12T13:37:02.991852Z","title":"Language models are unsu- pervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.991852Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:926bafc9fe53247a220568de09faf29a3c36f7e219c136a28d9700a6ed7cf5f0","observation_id":"8be75318-aba9-40ed-b3e6-a5adc49bc5c3","resolution":{"observed_at":"2026-08-12T13:37:02.991852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-12T13:37:02.996004Z","title":"Sam 2: Segment anything in images and videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:02.996004Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:d271874e1b56129a81ae84de6fee1c3989288daab0db3c22a5223c3369a6b932","observation_id":"498df00e-15cd-4268-ab34-16cb82173016","resolution":{"observed_at":"2026-08-12T13:37:02.996004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04592","last_updated":"2021-01-24T22:19:30Z","snapshot_observed_at":"2026-08-18T17:53:57.208912Z","submitted_at":"2020-10-09T14:18:53Z","title":"Contrastive Learning with Hard Negative Samples","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04592","snapshot_observed_at":"2026-08-12T13:37:03.002185Z","title":"Contrastive learning with hard negative sam- ples","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.002185Z"},"links":{"cited_paper":"/paper/2010.04592","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:74e9cc1965c02a2bb0cf5075e5b213aac94395b3721fb92607c9ebc4b1f9e6ae","observation_id":"b79fbbbf-786b-47d6-97dc-97e5c7e81792","resolution":{"observed_at":"2026-08-12T13:37:03.002185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04212","last_updated":"2024-08-12T20:34:05Z","snapshot_observed_at":"2026-08-18T18:23:53.826392Z","submitted_at":"2024-08-08T04:34:29Z","title":"Is SAM 2 Better than SAM in Medical Image Segmentation?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04212","snapshot_observed_at":"2026-08-12T13:37:03.008531Z","title":"Is sam 2 better than sam in medical image segmentation?arXiv preprint arXiv:2408.04212, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.008531Z"},"links":{"cited_paper":"/paper/2408.04212","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:dbede712a68ac1adf1bb1aabb2e32c89f5d2b93a32ce532ba48dcb9d09650af1","observation_id":"fd9d80a0-f2f5-49e5-be2c-e0a07fe737fd","resolution":{"observed_at":"2026-08-12T13:37:03.008531Z","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":"2026-08-12T13:37:03.783437Z","title":null,"venue":null,"work_id":"9a620a50-5215-4117-a3d2-c28ee7040043","year":2000},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.014597Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:11460c82d9c2b5efa56a0b7a577b31b7633647d8a3084bcdee1d606102a49da7","observation_id":"b57a2f8f-d5db-4933-a907-28c359881f09","resolution":{"observed_at":"2026-08-12T13:37:03.789437Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.764993Z","title":"Medical image registration based on uncoupled learning and accumulative enhancement","venue":null,"work_id":"6787f7fa-d2f5-473f-a75b-4b9ee26d7d40","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.022635Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:e07aec9d3146cac32b8b33ddc1d7495848deb39abfc568ac62a8ed329be8f47b","observation_id":"85889404-d324-4358-8448-685cf28e8e67","resolution":{"observed_at":"2026-08-12T13:37:03.771089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2203.15856","last_updated":"2022-03-29T18:57:23Z","snapshot_observed_at":"2026-08-18T18:24:08.796020Z","submitted_at":"2022-03-29T18:57:23Z","title":"OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15856","snapshot_observed_at":"2026-08-12T13:37:03.027486Z","title":"Odontoai: A human- in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.027486Z"},"links":{"cited_paper":"/paper/2203.15856","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:1df8e5b7089e83c5fce2246235049c9f364ecaa5501a65cde3180be3766b58b3","observation_id":"a0e9a9b8-1163-43bd-9218-b1d87f89d766","resolution":{"observed_at":"2026-08-12T13:37:03.027486Z","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":"2026-08-12T13:37:03.748120Z","title":"⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning","venue":null,"work_id":"629c49b9-e4c2-412a-8404-e88e1ec93efb","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.032978Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:f2772a8a68cebcb090d110f1c9077f4a06fa238fb04ed1db95e6919b07373ee8","observation_id":"bab0e869-efe8-400f-bee6-f73ffe8e3138","resolution":{"observed_at":"2026-08-12T13:37:03.754115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-12T13:37:03.037261Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.037261Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:7ba0b8e7175ec3def890ed5328da52c038734fd4757bf9c9fd7748b83e50ff19","observation_id":"303cd4c8-b554-4c3c-93bb-2168d7f930ed","resolution":{"observed_at":"2026-08-12T13:37:03.037261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-12T13:37:03.042700Z","title":"Llama 2: Open foundation and fine-tuned chat models.arXiv preprint arXiv:2307.09288, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.042700Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b6960e5461a306236219213068101adeb50f146c8210c9df8b2ef3630c4e3601","observation_id":"ec3c7134-521d-4773-9356-34275a6331b7","resolution":{"observed_at":"2026-08-12T13:37:03.042700Z","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":"2026-08-12T13:37:03.729775Z","title":"Multi-stage transfer learning for lung segmentation using portable x-ray devices for patients with covid-19","venue":null,"work_id":"7f42b1f7-b8c7-4d13-aa42-c9be1581ed2c","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.047817Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:656aeac08ae356c6a5ea15e6ac869aae6ece816ea5e71bc78ea11cec91ba56ff","observation_id":"6aab722e-f659-4bc6-8702-46f1210c5b74","resolution":{"observed_at":"2026-08-12T13:37:03.736305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.702889Z","title":"Images speak in images: A generalist painter for in-context visual learning","venue":null,"work_id":"f37db2ca-7440-4adf-aa49-f1b4143f865a","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.052682Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:8f7d0f1deb2cbf0cfe80250e195f933336c89fe2cd884876811b83dcbba00f02","observation_id":"53ffe957-ec84-4056-8b27-8e7d0c33b0a8","resolution":{"observed_at":"2026-08-12T13:37:03.709378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2304.03284","last_updated":"2023-04-06T17:59:57Z","snapshot_observed_at":"2026-08-16T15:42:11.267112Z","submitted_at":"2023-04-06T17:59:57Z","title":"SegGPT: Segmenting Everything In Context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.03284","snapshot_observed_at":"2026-08-12T13:37:03.056995Z","title":"Seggpt: Segmenting ev- erything in context","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.056995Z"},"links":{"cited_paper":"/paper/2304.03284","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:34858650fbdd995b4596d8e9a5005ac9fcbe1c1c8fe0162fe6de66e07322edc9","observation_id":"0d43a893-ffca-46bb-ac83-80014f23ed16","resolution":{"observed_at":"2026-08-12T13:37:03.056995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07682","last_updated":"2022-10-26T05:06:24Z","snapshot_observed_at":"2026-08-17T00:14:01.413100Z","submitted_at":"2022-06-15T17:32:01Z","title":"Emergent Abilities of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07682","snapshot_observed_at":"2026-08-12T13:37:03.068121Z","title":"Emergent abilities of large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.068121Z"},"links":{"cited_paper":"/paper/2206.07682","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:8026b5b38ae201c5cda161df1af36f4c8a1dc11b7f263baac03dd09567948f2d","observation_id":"53d697a8-05d1-4777-895a-9061e543043a","resolution":{"observed_at":"2026-08-12T13:37:03.068121Z","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":"2026-08-12T13:37:03.686307Z","title":"Chain-of-thought prompting elicits reasoning in large lan- guage models","venue":null,"work_id":"1b2ec678-e7c1-4235-a7cf-d7a0a745a199","year":2022},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.073814Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:0d4d1ed9146053357d0a6d9f55f78291f1af2e5edcf31c86cd86ebb290988e4e","observation_id":"c74ea57a-6438-466f-b234-a39b209248e7","resolution":{"observed_at":"2026-08-12T13:37:03.691179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.669905Z","title":"Prompting segment anything model with domain-adaptive prototype for generalizable medical image segmentation","venue":null,"work_id":"ab9a0c58-69c6-4652-963a-805cd593ccd6","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.078906Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:f522bf0c15f22b752639d75713006632d15975c326be3a96f2b50135e0ede4b2","observation_id":"f203d726-d1bb-4c1b-88b9-dd21b9cecc59","resolution":{"observed_at":"2026-08-12T13:37:03.674737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.656834Z","title":"Wong, Marianne Rakic, John Guttag, and Adrian V","venue":null,"work_id":"b2793c67-dbe9-42d2-a5ae-099025d10413","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.084647Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:1cbaefe7dbe03e3c9cba4611049362944e10ac1c7ef863825602e3def2ba2497","observation_id":"91ba9c29-b7e3-4184-8992-84ec61bd4f9f","resolution":{"observed_at":"2026-08-12T13:37:03.661433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-12T13:37:03.089668Z","title":"Medical sam adapter: Adapting seg- ment anything model for medical image segmentation, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.089668Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:ddeeddf60fc91d65568841eacfb0f1b7909d2bab9e48bdfda74de3032d0bf5e1","observation_id":"f0ccf7f6-a43d-4063-86e7-858a4488adb5","resolution":{"observed_at":"2026-08-12T13:37:03.089668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03631","last_updated":"2024-07-16T01:23:47Z","snapshot_observed_at":"2026-08-18T18:23:26.533366Z","submitted_at":"2024-02-06T02:00:18Z","title":"CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03631","snapshot_observed_at":"2026-08-12T13:37:03.094167Z","title":"Cat-sam: Con- ditional tuning for few-shot adaptation of segment anything model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.094167Z"},"links":{"cited_paper":"/paper/2402.03631","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:433ff664d030733d3109911e58d442e2d8062656e3139b453b6e91c66e0f8247","observation_id":"0d087ac6-c340-4cab-a64b-7e53816008fc","resolution":{"observed_at":"2026-08-12T13:37:03.094167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13785","last_updated":"2023-10-17T12:24:24Z","snapshot_observed_at":"2026-08-16T15:37:26.441651Z","submitted_at":"2023-04-26T19:05:34Z","title":"Customized Segment Anything Model for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13785","snapshot_observed_at":"2026-08-12T13:37:03.099147Z","title":"Customized segment any- thing model for medical image segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.099147Z"},"links":{"cited_paper":"/paper/2304.13785","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:963583c6127a0a5075b2dbd6090070425854a9195cd6f230670df6607abca740","observation_id":"6b0766ec-8b8f-49c8-b403-7f713af72de4","resolution":{"observed_at":"2026-08-12T13:37:03.099147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03048","last_updated":"2023-10-04T01:15:21Z","snapshot_observed_at":"2026-08-16T15:35:32.915462Z","submitted_at":"2023-05-04T17:59:36Z","title":"Personalize Segment Anything Model with One Shot","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03048","snapshot_observed_at":"2026-08-12T13:37:03.103901Z","title":"Person- alize segment anything model with one shot","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.103901Z"},"links":{"cited_paper":"/paper/2305.03048","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:516e8c96ba783657db3c5bdf8e6a2fad61119c26c046db6ea6b2fd11748eaa2f","observation_id":"25e95e48-be4a-424d-b887-31c28efea0d8","resolution":{"observed_at":"2026-08-12T13:37:03.103901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12889","last_updated":"2024-08-23T07:51:10Z","snapshot_observed_at":"2026-08-18T18:23:36.231137Z","submitted_at":"2024-08-23T07:51:10Z","title":"Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12889","snapshot_observed_at":"2026-08-12T13:37:03.109251Z","title":"Unleashing the potential of sam2 for biomedical images and videos: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.109251Z"},"links":{"cited_paper":"/paper/2408.12889","citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:576c05c1010ebf7050567dbd64cb404553503d1b644f05a388388399092df36d","observation_id":"033235cd-4951-4c8d-b6a9-2e2b1ed95e66","resolution":{"observed_at":"2026-08-12T13:37:03.109251Z","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":"2026-08-12T13:37:03.635513Z","title":"Semi-supervised cardiac image segmentation via label prop- agation and style transfer","venue":null,"work_id":"0dd9faad-f4a6-46c4-813c-eebbe0dcaba0","year":2020},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.116687Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:fc93336895df6843dd63c2f0a624c571ff3daa3b8eb96df85d04a1e6d684795d","observation_id":"6a7c83be-0812-436c-a146-ab7c0c97e384","resolution":{"observed_at":"2026-08-12T13:37:03.639813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.622446Z","title":"Can sam segment polyps?, 2023","venue":null,"work_id":"a8744dbd-5350-462d-b8ef-75de677ccc4e","year":2023},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.126124Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:2a8731c473821cd699b8e5c6d8e58409a3f2db14a6e8eadb983e4c0deea92baf","observation_id":"e7f1c599-56bd-41f4-b680-e1a483fa4a71","resolution":{"observed_at":"2026-08-12T13:37:03.626705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.609510Z","title":"Test-time training for deformable multi-scale image registration","venue":null,"work_id":"131715f5-48ea-448b-84ad-bfb66b8f99e4","year":2021},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.135651Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:c3a0035cc1d5808c4a7d8a849b3556256301cd5bbb1568e20b8df63a5347418e","observation_id":"19b1a31d-3bf8-42f6-9d71-5db843fe7433","resolution":{"observed_at":"2026-08-12T13:37:03.613745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.595693Z","title":"Segment everything everywhere all at once","venue":null,"work_id":"aa385544-c8a8-4725-b934-20cb50f08960","year":2024},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.140686Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:b986dc9a61874c3b867a396962f5aadab94db98b0480f9a137189808b6175c72","observation_id":"66a4788c-1856-4428-ba08-356fe6c97dbb","resolution":{"observed_at":"2026-08-12T13:37:03.600518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:37:03.581501Z","title":"target-semantic prompting","venue":null,"work_id":"d4bda9f7-f939-4637-8636-e5516bc6a23e","year":null},"citing_paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T13:37:03.145694Z"},"links":{"citing_paper":"/paper/2411.16123"},"observation_digest":"sha256:09a87b5f38e60eeb1463652258dfd59a978491d5aef761f9b78e5b9434081951","observation_id":"34d8b9a5-ff30-4226-98aa-0754170aee50","resolution":{"observed_at":"2026-08-12T13:37:03.586280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2411.16123","last_updated":"2024-11-25T06:16:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T17:55:03.598314Z","submitted_at":"2024-11-25T06:16:17Z","title":"Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":2,"verified_fuzzy":39},"total_outbound_references":73},"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 21 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2411.16123."}