{"as_of":"2026-08-08T17:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:59a8b448ef771edbdea8e26593adda6a879b2a84a6bfbde35f73c3569be8413d","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:12:46.327990Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2506.19590/citation-record","integrity":"/paper/2506.19590/integrity","json":"/paper/2506.19590/citation-record.json","paper":"/paper/2506.19590"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.185096Z","title":"epub 2023 Apr","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.185096Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:8f548fdb4b74abe668bd2d96dbeba8cda30623cf60adf4367205ed08fb55d953","observation_id":"c260fc5e-d699-4110-af43-5d3378523049","resolution":{"observed_at":"2026-08-06T23:12:46.185096Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.02701","last_updated":"2022-11-04T18:35:00Z","snapshot_observed_at":"2026-07-06T14:14:38.788226Z","submitted_at":"2022-11-04T18:35:00Z","title":"MONAI: An open-source framework for deep learning in healthcare","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.02701","snapshot_observed_at":"2026-08-06T23:12:46.226023Z","title":"Kim, D.H., Seo, J., Lee, J.H., Jeon, E.T., Jeong, D., Chae, H.D., Lee, E., Kang, J.H., Choi, Y.H., Kim, H.J., Chai, J.W.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.226023Z"},"links":{"cited_paper":"/paper/2211.02701","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:92ad159b80002aaa0ffd6f2e5ba70e8834a95a831a0bbd4822029e6b088c1998","observation_id":"19a64877-32f6-483d-9433-5419e45cfc3f","resolution":{"observed_at":"2026-08-06T23:12:46.226023Z","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":"2023.0671","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:47.185587Z","title":"Korean Journal of Radiology 25, 363–373","venue":null,"work_id":"52ef44e0-b7fb-4885-8d7f-6d432af948f7","year":2023},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.231345Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:1d7595144ca6dbc5f6bcbedc7915a46bf36a6172cbb0ce2d31afe6b50fbfa419","observation_id":"a17058c4-9439-463e-a31e-3c8d4b25d666","resolution":{"observed_at":"2026-08-06T23:12:47.244388Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11253","last_updated":"2025-01-20T03:34:49Z","snapshot_observed_at":"2026-08-06T20:27:41.973544Z","submitted_at":"2025-01-20T03:34:49Z","title":"How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11253","snapshot_observed_at":"2026-08-06T23:12:46.236404Z","title":"URL:https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.236404Z"},"links":{"cited_paper":"/paper/2501.11253","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:d1e462309fb834d52fe7efd7b2a8c1f5df8ec709625202c9282724c9c9452d21","observation_id":"e92cd66e-7b89-4115-be00-9b17b62631ed","resolution":{"observed_at":"2026-08-06T23:12:46.236404Z","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":"2021.77253","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.931830Z","title":"Frontiers in Oncology 11, 772530","venue":null,"work_id":"397d8d48-fd85-41b6-ad49-7fb87c5e5a0a","year":2021},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.244745Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:ab679b49d786bcae94ede4330dfcbe5001253411b1c7220192c0dd555deb7467","observation_id":"2642b54e-1668-450d-ad76-5542459d29e7","resolution":{"observed_at":"2026-08-06T23:12:47.034758Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.14791","last_updated":"2022-03-28T07:53:21Z","snapshot_observed_at":"2026-07-06T12:13:17.374586Z","submitted_at":"2021-11-29T18:45:20Z","title":"Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.14791","snapshot_observed_at":"2026-08-06T23:12:46.281816Z","title":"Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.281816Z"},"links":{"cited_paper":"/paper/2111.14791","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:6d800bd2a3ca3101ec35028872314c6426683adca7fdd3875fe6cb24af529a3d","observation_id":"6aed0036-cc63-495e-9de7-62fdc2918eb4","resolution":{"observed_at":"2026-08-06T23:12:46.281816Z","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":"10.1002/jmri.27485","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.370627Z","title":"Journal of Magnetic Resonance Imaging 55, 653–680","venue":null,"work_id":"0b59abfb-6de2-4a8f-a890-4230e51e0a78","year":2020},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.298108Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:14dbb4b368732f2e1f77e09bf0a69dc3f531c11d1329c9413e3cdfa4740464e3","observation_id":"a46fac4c-8029-4064-9c69-b5f38fb8c15f","resolution":{"observed_at":"2026-08-06T23:12:46.377594Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17041","last_updated":"2025-04-18T13:14:59Z","snapshot_observed_at":"2026-08-04T14:02:02.639930Z","submitted_at":"2024-12-22T14:38:28Z","title":"An OpenMind for 3D medical vision self-supervised learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17041","snapshot_observed_at":"2026-08-06T23:12:46.303081Z","title":"arXiv preprint arXiv:2412.17041 URL: https://arxiv.org/abs/2412.17041","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.303081Z"},"links":{"cited_paper":"/paper/2412.17041","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:894e7ee972fc0b17384f69b2397a3ac7aabcc3770952f771cbbb51d7ca18cf1d","observation_id":"21fa91b3-9ccd-40f8-aba8-aee1171ca9b2","resolution":{"observed_at":"2026-08-06T23:12:46.303081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.05868","last_updated":"2023-06-16T14:26:43Z","snapshot_observed_at":"2026-08-08T09:04:21.631864Z","submitted_at":"2022-08-11T15:16:40Z","title":"TotalSegmentator: robust segmentation of 104 anatomical structures in CT images","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.05868","snapshot_observed_at":"2026-08-06T23:12:46.308288Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.308288Z"},"links":{"cited_paper":"/paper/2208.05868","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:8d68d9cb41f9ef9bd8c7a973cea5ac0d20bad8e5c400de782777bb675f889ed1","observation_id":"2bc7a685-749a-404f-9f9b-7db6c74c425e","resolution":{"observed_at":"2026-08-06T23:12:46.308288Z","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":"2020.10184","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.621770Z","title":"Medical Im- age Analysis 67, 101840","venue":null,"work_id":"915cb837-a851-4105-9173-105de95fabe6","year":2020},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.327990Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:6ccf326441dfa0e90013f091bbf185b6175de0e47cd9d4c4e3c907e828dd7244","observation_id":"e8efcd0c-aeef-410d-a45f-52641dfae89d","resolution":{"observed_at":"2026-08-06T23:12:46.649825Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s13244-022-01287-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.467165Z","title":"pMID: 36194301; PMCID: PMC9525241","venue":null,"work_id":"17ac2de7-758f-47a8-9374-b440dbbcde5f","year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":159,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.197432Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:93733ab46736134dd6cbc20fa993a5dabe5bfecee1b25b99c995b16a8fc7da20","observation_id":"fa0a4fd3-f4b9-458f-b3bc-91709694766d","resolution":{"observed_at":"2026-08-06T23:12:46.474981Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:46.313161Z","title":"Biometrics Bulletin 1, 80–83","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":1945,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.313161Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:c71b53a79d2b533b5b6c106ecf88a21cbe55b98e1cdbc831ea2ede19146767c5","observation_id":"7ba45dee-9a00-4f72-8da9-acf0d5dff6ac","resolution":{"observed_at":"2026-08-06T23:12:46.313161Z","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":"10.1007/978-94-017-1699-4_3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.388975Z","title":"Springer Netherlands, Dor- drecht","venue":null,"work_id":"5bd440b4-965f-4e3f-9a27-48452721b58c","year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":1994,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.277119Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:17f7859ec06e82985b8cf55dddc75dd62b6753a2ef28d1e03084b133801a1b16","observation_id":"f33b3406-e1db-41c4-ad69-9f70c340dd63","resolution":{"observed_at":"2026-08-06T23:12:46.394342Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:46.293003Z","title":"IEEE Transactions on Medical Imaging 29, 1310–1320","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.293003Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:9829332ed97d5c4b53fd3cc49468de2cf52165d01a0748dabb9a951a975d9b43","observation_id":"3fb22f47-a655-40da-af7b-7f67f5807d25","resolution":{"observed_at":"2026-08-06T23:12:46.293003Z","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":"2026-08-06T23:12:46.202737Z","title":"Magnetic Resonance Imaging 30, 1323–1341","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.202737Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:c10e945a2554fe77c028df71f672c71d12c0bb2a2068613c5b9bdbb56eb47350","observation_id":"a243616d-8878-4fc3-99b9-1e6686a1a5df","resolution":{"observed_at":"2026-08-06T23:12:46.202737Z","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":"10.1148/radiol.14141242","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.409194Z","title":"Radiology 275, 155–166","venue":null,"work_id":"a4781a41-23cb-4fe3-817a-c165b4b39997","year":2014},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.265046Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:a542a7f9293dc13813ab6a56d655b96b0769d6c8a91cfdade809498a11737e05","observation_id":"3ac8a5af-839c-47be-823b-f397e9fe5704","resolution":{"observed_at":"2026-08-06T23:12:46.416693Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:47.672596Z","title":"3342–3345","venue":null,"work_id":"f962d104-8fa3-446d-aefe-4169fcd395f7","year":2016},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.317960Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:e5c442ca470018cf843a6bdbf405a98a3a1d38acc885967bc5a5adc14c4501f6","observation_id":"3c46a422-1c4c-41d3-901f-28d2869f6be7","resolution":{"observed_at":"2026-08-06T23:12:47.760792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.1169361","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.432103Z","title":"URL: https://doi.org/10.5281/zenodo.1169361, doi:10.5281/zenodo.1169361","venue":null,"work_id":"229f3ae6-1c28-4731-8c6a-b88cf62afa75","year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.208232Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:a20fb91a94e8d0c4dd6ca72f9c1e7a9986f20872c91e37ac7af8eb7b2ade8e37","observation_id":"65df0e7c-2b5c-46fe-bb6f-3259298f3ee4","resolution":{"observed_at":"2026-08-06T23:12:46.441177Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/mrm.27879","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:46.499588Z","title":"Magnetic Resonance in Medicine 82, 1872–1884","venue":null,"work_id":"faecaf13-d9b5-4243-8906-67ec73db60e1","year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.167758Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:b4879e01dc1e2b3312cd7f9e841a5a907e6386fd7c19673aacb291226266b617","observation_id":"e4a21e27-2d5a-4488-868f-a58041faf381","resolution":{"observed_at":"2026-08-06T23:12:46.508874Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:47.847698Z","title":"(Eds.), Medical Imaging 2020: Computer-Aided Diagnosis, SPIE","venue":null,"work_id":"45c8f708-e6d3-4b5f-8f3a-d7d91016a493","year":2020},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.172863Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:debf770c9c483c371c8bb985f4c81b28b28733c5e3f25edb224d21c76f51363f","observation_id":"0f228dae-595a-44d3-b258-57fd73b9ffaf","resolution":{"observed_at":"2026-08-06T23:12:47.940087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:46.191517Z","title":"Physiological Reviews 101, 797–855","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.191517Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:46a9d6fde9141e48478fd25ee2ba3bb2f4a2a38099e3e726629c11b29861b9f0","observation_id":"1cbcb047-d886-483f-a549-fffd0722d0fe","resolution":{"observed_at":"2026-08-06T23:12:46.191517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.01266","last_updated":"2022-01-04T18:01:34Z","snapshot_observed_at":"2026-08-06T22:15:15.327794Z","submitted_at":"2022-01-04T18:01:34Z","title":"Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.01266","snapshot_observed_at":"2026-08-06T23:12:46.213440Z","title":"He, K., Chen, X., Xie, S., Li, Y., Doll´ ar, P., Girshick, R.B.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.213440Z"},"links":{"cited_paper":"/paper/2201.01266","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:370c93d9bb215181d9e157c559078e3343dd0d59a2a560dcafd65d9215fa83aa","observation_id":"dfc5afd6-330f-4ac1-bf3c-41c7c46ef457","resolution":{"observed_at":"2026-08-06T23:12:46.213440Z","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":"2026-08-06T23:12:46.323589Z","title":"Medical Image Analysis 89, 102879","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.323589Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:b02ec02cb5c2f54a936a68b40c37a7125ae490b59a12928c0649388e8cd0fcdf","observation_id":"a5d1e372-75c6-4f7d-a481-11d5941abb68","resolution":{"observed_at":"2026-08-06T23:12:46.323589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12974","last_updated":"2024-01-23T18:59:25Z","snapshot_observed_at":"2026-08-05T01:27:30.973789Z","submitted_at":"2024-01-23T18:59:25Z","title":"SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI","version":1},"cited_work":{"arxiv_id":"2401.12974","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.12974","snapshot_observed_at":"2026-08-06T23:12:47.372138Z","title":"SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI","venue":"eess.IV","work_id":"960c211f-2b07-4c7a-b8c5-b5415c83d1ea","year":2024},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.219561Z"},"links":{"cited_paper":"/paper/2401.12974","citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:5d1cc01c47087883e1cf4053baf1b3759ae46f7b21853c55704e3d4b658fbe17","observation_id":"08a291ec-4efe-4ae2-9e24-199225c34afd","resolution":{"observed_at":"2026-08-06T23:12:47.448444Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T23:12:46.161745Z","title":"Armstrong, R.A.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI","version":1},"reference_index":4128,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:46.161745Z"},"links":{"citing_paper":"/paper/2506.19590"},"observation_digest":"sha256:7931b5d5542cb8ce6904720110a34688add14437200bc1afcd513af56bd63bfc","observation_id":"246396c5-2618-432a-aaae-26fa6476aada","resolution":{"observed_at":"2026-08-06T23:12:46.161745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19590","last_updated":"2025-06-24T12:59:44Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-08T15:45:26.709287Z","submitted_at":"2025-06-24T12:59:44Z","title":"Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":12,"verified_exact":7,"verified_fuzzy":2},"total_outbound_references":25},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.19590."}