{"as_of":"2026-08-21T07:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d4a94f2b0457d478fdc8a61ff28280b91b32cf22269c092bd42bf73852f4e3a9","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:57:53.187157Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2505.08527/citation-record","integrity":"/paper/2505.08527/integrity","json":"/paper/2505.08527/citation-record.json","paper":"/paper/2505.08527"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:57:53.781596Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":"511d126f-7beb-422c-9022-218e75b75509","year":2015},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.016422Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:369b510b8acf28da05b982012945eb1b896364c3fd623c587ab55c30fbc24f7b","observation_id":"2d9815a2-3d33-4a91-b480-dfcf7b6d9f51","resolution":{"observed_at":"2026-08-15T21:57:53.785793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.769050Z","title":"Medical image segmentation using deep learning: A survey,","venue":null,"work_id":"d812cc71-5e8e-4521-9fae-f15ef2ca79a0","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.021283Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:926b668b6e4a2b1f615605bd4810df2b5efdb5ff0fa6c7bbbb116cc365f5be05","observation_id":"e5413d57-7419-46a6-ada8-0f554d704570","resolution":{"observed_at":"2026-08-15T21:57:53.773101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.756003Z","title":"A-eval: A benchmark for cross-dataset and cross- modality evaluation of abdominal multi-organ segmentation,","venue":null,"work_id":"2dcd4ab8-59f3-49ef-b7f1-b517f271c12d","year":2025},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.025437Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:9c0bdb61fc7f6407686c363612ac36635fb7268e551f9e4270d860f9d768f743","observation_id":"3bea771c-06f3-495b-a5ec-aadeae5250a2","resolution":{"observed_at":"2026-08-15T21:57:53.760433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.742635Z","title":"Patch-based output space adversarial learning for joint optic disc and cup segmentation,","venue":null,"work_id":"e5932bb7-bdaa-4748-b204-aa5d922ea814","year":2019},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.029779Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:f7f9f8db929dd7ee474f18ef0dd377b42abe6b20d3778ae53529019d2b9caa3d","observation_id":"a2721f5d-4325-47af-91ee-9e8e4e157208","resolution":{"observed_at":"2026-08-15T21:57:53.747042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.729294Z","title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,","venue":null,"work_id":"04a72a72-0fd1-4901-b174-4295b3dfe1db","year":2020},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.033702Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:31900f7f1dbe900e206f3af6de9029de4db2cc3f356ae076a10f3fcccf361003","observation_id":"2893e07f-97cc-485f-bb28-d7ad462b15f7","resolution":{"observed_at":"2026-08-15T21:57:53.733745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.715669Z","title":"Source-free domain adaptation via distribution estimation,","venue":null,"work_id":"e1c99d2a-1acc-490a-91bc-17ee96488e77","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.038131Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:8978140fa51915fc7d42e8ec948a960fff3959a9cd82916eae3c215ae87e248d","observation_id":"57071726-375e-4d23-91b4-0a02e3b56a21","resolution":{"observed_at":"2026-08-15T21:57:53.720264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.702349Z","title":"Uncertainty reduction for model adaptation in semantic segmentation,","venue":null,"work_id":"4da17f86-a4c1-4ddd-ac76-6cecb0297006","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.043617Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:9d8f03cf08eada49e9f933a9676f64ff9ca743d8a6fa7a1463ac00ddb8dd2164","observation_id":"ea8cf806-66d4-4384-8a16-8005a81806ec","resolution":{"observed_at":"2026-08-15T21:57:53.706613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.690028Z","title":"Uncertainty-guided source-free domain adaptation,","venue":null,"work_id":"c05f07c5-2ec5-400e-8e77-8f389c2ae7de","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.047378Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:e263a6f880ae923def3ee87ece845264ca46bb61b4a77ce6bb284129c1520a09","observation_id":"6835f6b7-1c98-47d2-818b-bea8c86547d3","resolution":{"observed_at":"2026-08-15T21:57:53.693979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.677141Z","title":"Class relationship embedded learning for source-free unsupervised domain adaptation,","venue":null,"work_id":"adcd4669-6515-4ee4-a440-2125655ce1fe","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.051134Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:566dfa3fa2c1177e839fb477f1262e8f70cfbf7f1f734508f9ad7603501bfacb","observation_id":"382d6da1-a3d9-4bd8-ac60-55409b0ee9b5","resolution":{"observed_at":"2026-08-15T21:57:53.681343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.664401Z","title":"Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling,","venue":null,"work_id":"7f8e299e-7c98-45b4-8df8-47e64dfc9164","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.055038Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:3ff5c2514f99399970e7f7bc9d34199e1dce78c2fc13ef193b1a8b8edeca3f1e","observation_id":"f7460425-1712-4688-9df3-95cad92ad793","resolution":{"observed_at":"2026-08-15T21:57:53.668640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.651589Z","title":"Denoising for relaxing: Unsupervised domain adaptive fundus image segmentation without source data,","venue":null,"work_id":"0f6773ec-8582-428f-8eb8-9ac5fa9987aa","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.058877Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:cc836e2660e6734e76cf6431b709ec5e1eac6d49ce0e82b11b47995852588aa8","observation_id":"48d62991-6993-4978-b5a2-cbc33df458b4","resolution":{"observed_at":"2026-08-15T21:57:53.655824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.638285Z","title":"Upl-sfda: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation,","venue":null,"work_id":"d66f2467-4e48-4b22-8ec0-78498ce9223a","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.062911Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:dd8f84126be79b9f84e70f3622f3653cafd50c04394853d60b90a5eecedacc3c","observation_id":"6b4db738-18ba-4e98-b47e-1fbecee43f2f","resolution":{"observed_at":"2026-08-15T21:57:53.642524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.625218Z","title":"Source free domain adaptation for medical image segmentation with fourier style mining,","venue":null,"work_id":"db53cef5-9afc-40d9-a631-cef6b0223e35","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.067055Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:e0782d4c8b2831fb07df4ef7b3a5f821a6c3f1d224590e0c8fdad6bc7cee0c2e","observation_id":"f95f356b-fd04-4da5-bcf3-a93489237a8f","resolution":{"observed_at":"2026-08-15T21:57:53.629558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.611060Z","title":"Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation,","venue":null,"work_id":"d6e3b6c1-43da-4e47-930d-05b78e8b59b5","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.071210Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:f9f6297ea1ccb8d57a14be2f940276a5e6c914bcdfccd0b87a726a0a1a6ac802","observation_id":"1f95e146-47e9-49e1-a56c-ff681b07615a","resolution":{"observed_at":"2026-08-15T21:57:53.615632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.598038Z","title":"Source- free domain adaptation for image segmentation,","venue":null,"work_id":"38683950-a366-49ec-acbf-a4e1a43f1245","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.075017Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:bb7711cb5534be0bf67c78aceb5c799d5612704618281704fe3c97ba42d9426b","observation_id":"07f7d5db-1e94-4053-8b60-c09462e771cf","resolution":{"observed_at":"2026-08-15T21:57:53.602301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.584240Z","title":"Source- free domain adaptation for medical image segmentation via prototype- anchored feature alignment and contrastive learning,","venue":null,"work_id":"0f5f4f95-0c49-4102-9848-32bb79408659","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.078870Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:116f34d891e15699827b1568d1e60f54599031ee7a4e670008021633745c5eb3","observation_id":"b37c336a-5f2a-4fd3-847a-da048a9a0a15","resolution":{"observed_at":"2026-08-15T21:57:53.588971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.570095Z","title":"Segment anything,","venue":null,"work_id":"dc7b4eaf-9977-4ea3-8c60-1bd51ca9f853","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.082946Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:455721b0ea79e55f27f34fe33f992957d2e5862cb8e8a7c9dbb0d2059e109ca5","observation_id":"7fd02b93-cfea-442b-8981-523a02e809d4","resolution":{"observed_at":"2026-08-15T21:57:53.574052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-15T21:57:53.087062Z","title":"Customized segment anything model for medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.087062Z"},"links":{"cited_paper":"/paper/2304.13785","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:0018f13787d6714ea9bcc3e0d9f268bb24a9c250a7a26199aad4537e17b82462","observation_id":"73bd3276-f937-42be-8d6b-0bdc2e045990","resolution":{"observed_at":"2026-08-15T21:57:53.087062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.08842","last_updated":"2023-09-16T02:41:53Z","snapshot_observed_at":"2026-08-16T15:00:13.774161Z","submitted_at":"2023-09-16T02:41:53Z","title":"MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.08842","snapshot_observed_at":"2026-08-15T21:57:53.091251Z","title":"Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.091251Z"},"links":{"cited_paper":"/paper/2309.08842","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:f58a915ba7c262b9f79fb84de07de2ea8868f98a32b7f5114dc3f61c91dcfcd7","observation_id":"58ac12b0-3f29-4894-a299-e038c55e11c4","resolution":{"observed_at":"2026-08-15T21:57:53.091251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12620","last_updated":"2023-12-29T03:40:59Z","snapshot_observed_at":"2026-08-19T01:36:43.080792Z","submitted_at":"2023-04-25T07:34:22Z","title":"Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12620","snapshot_observed_at":"2026-08-15T21:57:53.095356Z","title":"Medical sam adapter: Adapting segment anything model for medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.095356Z"},"links":{"cited_paper":"/paper/2304.12620","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:b8dfe754b8ac894ea68ed94de48eb12d52d3845c3b1120b243c3d3d6e8572e8a","observation_id":"60b038ec-f0c0-422f-96fa-cbaa02c9a91e","resolution":{"observed_at":"2026-08-15T21:57:53.095356Z","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":"2026-08-15T21:57:53.556822Z","title":"Segment anything in medical images,","venue":null,"work_id":"4982ff90-c390-4000-abb8-24f139bec807","year":2024},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.099503Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:8896958b929766f689959a43eaa359e150b83a2d98fcffe4cff152fdf3746dcc","observation_id":"ad5e900f-1e40-431a-9a3f-3c2560ee5d66","resolution":{"observed_at":"2026-08-15T21:57:53.561140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15161","last_updated":"2024-09-14T05:30:41Z","snapshot_observed_at":"2026-08-19T19:45:57.292611Z","submitted_at":"2023-10-23T17:57:36Z","title":"SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15161","snapshot_observed_at":"2026-08-15T21:57:53.103312Z","title":"Sam-med3d,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.103312Z"},"links":{"cited_paper":"/paper/2310.15161","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:e36a8b44e580cf2eba2cce16101812b8bc1f479ff97d3bd8fb3c9acf80f5757e","observation_id":"9838ee6a-1e6e-4f68-bbfc-07d9083ca7a6","resolution":{"observed_at":"2026-08-15T21:57:53.103312Z","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":"2026-08-15T21:57:53.543523Z","title":"Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting,","venue":null,"work_id":"b0cf7607-b59c-4e55-b06f-699f48b48023","year":2024},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.107436Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:af2cb77f150bbad3efb125eeb9f62d52c663863eafc1d494c123068dd1d821cc","observation_id":"1a2964cd-5d63-48d3-b367-9c3ece910ecf","resolution":{"observed_at":"2026-08-15T21:57:53.547971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00924","last_updated":"2024-09-02T03:40:07Z","snapshot_observed_at":"2026-08-19T13:05:49.687680Z","submitted_at":"2024-09-02T03:40:07Z","title":"MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00924","snapshot_observed_at":"2026-08-15T21:57:53.111751Z","title":"Medsam-u: Uncertainty-guided auto multi-prompt adaptation for reliable medsam,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.111751Z"},"links":{"cited_paper":"/paper/2409.00924","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:fbd35ea79d851bdca9f7180f647dc0f1e09265c12ea468a00240d2e5c33db84d","observation_id":"5d6ade90-a861-47ff-af42-7bbf49fb457d","resolution":{"observed_at":"2026-08-15T21:57:53.111751Z","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":"2026-08-15T21:57:53.530419Z","title":"Source-free domain adaptive fundus image segmentation with class-balanced mean teacher,","venue":null,"work_id":"fa88b9b0-4b02-4a7f-a4a4-1e060b5b349f","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.116348Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:6f9ca56512d32fdf945ed7d56562c1e35b5ebbc041ebeea806aaebcbb9ddc860","observation_id":"05121d0d-64f4-4f1b-9789-ddb6940957b2","resolution":{"observed_at":"2026-08-15T21:57:53.534738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.516968Z","title":"Adapting off-the- shelf source segmenter for target medical image segmentation,","venue":null,"work_id":"e0d30d5e-e44f-40c3-b5bf-febbbe29b6fb","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.120283Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:eead0651d3ddaa457c182a41e964019205bd0c11c80171ab3cb796621d775d75","observation_id":"ebc13e71-0c0e-4ee5-a6de-ce197156b13a","resolution":{"observed_at":"2026-08-15T21:57:53.521567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.124228Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.124228Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:6441f51fdb9f2b046d4a70f1f9c77fb72be667cefb905af9dbb7ab27ee94d805","observation_id":"48557c3f-3e07-4150-b8b2-28e3e16faf91","resolution":{"observed_at":"2026-08-15T21:57:53.124228Z","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":"2026-08-15T21:57:53.494268Z","title":"Scaling up visual and vision-language representation learning with noisy text supervision,","venue":null,"work_id":"696f02c3-725d-4ad6-9b24-e853470c0389","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.128400Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:fc15c0bcb265ff2eaea1282372560c03e545753302a6ef6a9e665f2abec70b9e","observation_id":"5f2f931e-3f21-4649-93df-c3d7d081950d","resolution":{"observed_at":"2026-08-15T21:57:53.498936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.132171Z","title":"Seggpt: Segmenting everything in context,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.132171Z"},"links":{"cited_paper":"/paper/2304.03284","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:33a17a8bc77ecd0a80f8737c3e5796b5d59cc888881c64ac96ef6e08da8f1651","observation_id":"989df311-68ef-4970-a31d-1b4459148c62","resolution":{"observed_at":"2026-08-15T21:57:53.132171Z","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":"2026-08-15T21:57:53.480130Z","title":"Rethinking the role of pre-trained networks in source-free domain adaptation,","venue":null,"work_id":"a629b5d8-d27b-467f-af6c-07f4a10d2dc6","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.136424Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:c82769eb595b9de92f9da4a413a7e18f38d1e0e58c9c762fae670fa574c8f1f9","observation_id":"d8ae915c-071c-4a9d-8ab1-f68e5bd69b99","resolution":{"observed_at":"2026-08-15T21:57:53.484577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.466197Z","title":"The unreasonable effectiveness of large language-vision models for source- free video domain adaptation,","venue":null,"work_id":"7888e2c9-efb6-4357-9805-5b3d0dab5ba4","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.140078Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:036165dd181a8ee6b7047090d27765b249a50101046b0209a9b8456801063fc2","observation_id":"a9189b17-bb1c-465b-9d99-febaa226273c","resolution":{"observed_at":"2026-08-15T21:57:53.471094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.452606Z","title":"Source-free domain adaptation with frozen multimodal foundation model,","venue":null,"work_id":"4f6b2e5a-5d18-40dc-a1e3-943d913d8e54","year":2023},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.143713Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:6b6fab838d8d2d91275c258380d71bf1696482f0f06734184bbfad699db0b314","observation_id":"26ea47ce-ab2c-4825-9e57-7788537c0d41","resolution":{"observed_at":"2026-08-15T21:57:53.457199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.439205Z","title":"Exploiting the intrinsic neighborhood structure for source-free domain adaptation,","venue":null,"work_id":"5c150212-a01f-4410-b66f-647f7eb72f2e","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.147774Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:c9d11ff8d30e2aa3d236f57e1eac3e94e664bf61eedf6f7645b3f67b69093072","observation_id":"c2527c72-7ab6-46ee-a1f4-3188fd6aeff8","resolution":{"observed_at":"2026-08-15T21:57:53.443684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02811","last_updated":"2022-07-18T08:00:22Z","snapshot_observed_at":"2026-08-16T17:08:53.149600Z","submitted_at":"2022-04-06T13:23:02Z","title":"BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation","version":2},"cited_work":{"arxiv_id":"2204.02811","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.02811","snapshot_observed_at":"2026-08-15T21:57:53.220083Z","title":"BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation","venue":"cs.CV","work_id":"2b8bb01c-a6d7-4ed7-866b-9daea10cbe7b","year":2022},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.151763Z"},"links":{"cited_paper":"/paper/2204.02811","citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:cc6c158b53eae0ff322e211ce305021220d17449b5d6151381345e8e03e6fb6d","observation_id":"59cfcf28-1e62-44ee-a6c4-33d7c850ebc6","resolution":{"observed_at":"2026-08-15T21:57:53.225904Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.425424Z","title":"Exploiting chain rule and bayes’ theorem to compare probability distributions,","venue":null,"work_id":"c5fcdf1a-9eeb-488d-b7cb-0df52443e505","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.156511Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:846b50d6c26485b77137c265e8223fdc42c25a5df82675afd432dc31e594c759","observation_id":"6cd3d0c7-c6b0-4aa5-864e-62356df615d1","resolution":{"observed_at":"2026-08-15T21:57:53.429741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.160405Z","title":"Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.160405Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:8fbad48a2afc3ac199c24669a33728443699fcb92979948290cbef51e85f2d7a","observation_id":"51ba4f69-2b57-4b11-8439-4c50bf3b219c","resolution":{"observed_at":"2026-08-15T21:57:53.160405Z","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":"2026-08-15T21:57:53.402956Z","title":"Chaos challenge- combined (ct-mr) healthy abdominal organ segmentation,","venue":null,"work_id":"1737e329-af65-4026-bf23-7fcf15a84e45","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.164283Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:5dd2d7c89a48a6d3f4c610767bdb128ee002350578c14edc15b41f362d94f4b9","observation_id":"1d51b96b-710d-471f-8d25-0b5390f3773d","resolution":{"observed_at":"2026-08-15T21:57:53.407788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.388864Z","title":"Miccai calibration and uncertainty for multirater volume assessment in multiorgan segmentation challenge,","venue":null,"work_id":"9664d46c-b9fb-4728-99b3-936c1d68f928","year":2024},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.167895Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:5dfa77a029de6dd027ba17110f6b0cf3d0c228a93e277011fc28f8225f3b40e8","observation_id":"2bc6b748-51f5-4ec6-baf3-d2bcf7133106","resolution":{"observed_at":"2026-08-15T21:57:53.393737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.373811Z","title":"Domain adaptation meets zero-shot learning: an annotation-efficient approach to multi-modality medical image segmentation,","venue":null,"work_id":"4c03f717-1eea-4649-b647-df49cda42692","year":2021},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.171945Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:1a355e6ba3495a49bcf7bafce81caa0a1b54d410171fb5cdf23111332f98c966","observation_id":"057dea52-f265-47a0-a418-7a9e0f0be771","resolution":{"observed_at":"2026-08-15T21:57:53.378543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.359402Z","title":"Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,","venue":null,"work_id":"f95b0843-cef2-4a1e-a4bf-f68dcfd515ce","year":2020},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.175958Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:177ad53be9eb86f19c658d103effdf0a975481899ab8bad4b97606f5ceb03154","observation_id":"7ac9d6fb-077b-4955-9653-c471c32d4828","resolution":{"observed_at":"2026-08-15T21:57:53.363900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.345718Z","title":"Nci- isbi 2013 challenge: Automated segmentation of prostate structures,","venue":null,"work_id":"10dcec49-8818-42ee-81a4-4cd5551eda2d","year":2013},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.179660Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:2d0b9d0108bf4986268d5e3167642d725316a8df4e155a5d8c9d3de214d9cb0c","observation_id":"cb2ce17b-c685-45b9-b6f9-78d0792f1385","resolution":{"observed_at":"2026-08-15T21:57:53.350232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.332018Z","title":"Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data,","venue":null,"work_id":"d92885f2-9e2d-48ef-8a60-00025ebeb0b3","year":2020},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.183466Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:51369fb8a04265fb63d440acadb3ee4cb545b77f7785dbf6e17238c1eb1edad3","observation_id":"bb941f48-a3cf-4b9c-935d-915510e895e5","resolution":{"observed_at":"2026-08-15T21:57:53.336547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T21:57:53.317579Z","title":"Variability of manual segmen- tation of the prostate in axial t2-weighted mri: a multi-reader study,","venue":null,"work_id":"5dd29f72-1731-4e96-9830-d9eeb51dbc15","year":2019},"citing_paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T21:57:53.187157Z"},"links":{"citing_paper":"/paper/2505.08527"},"observation_digest":"sha256:023ce5bed6be68050e8d5866f1472f8149ea86c0e0b864f9e5ce5fd1f29d31b2","observation_id":"473725c1-6ab8-4fab-baca-0ba10d05dde6","resolution":{"observed_at":"2026-08-15T21:57:53.322224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.08527","last_updated":"2025-07-12T11:00:59Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T19:46:31.817387Z","submitted_at":"2025-05-13T13:00:48Z","title":"Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":34},"total_outbound_references":43},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2505.08527."}