{"as_of":"2026-08-15T07:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32ba709a52d5ab3811248bbacbfbd07a8462538004a1619b61f439a2197b563f","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T17:45:07.090571Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2508.15904/citation-record","integrity":"/paper/2508.15904/integrity","json":"/paper/2508.15904/citation-record.json","paper":"/paper/2508.15904"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T17:45:11.474494Z","title":"Machine learning-driven histotype diagnosis of ovarian carcinoma: Insights from the ocean ai challenge.medRxiv, pages 2024–04, 2024","venue":null,"work_id":"6fc1116c-6d45-4c2b-935d-c33e9fe778df","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.239071Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:2298bdd1ad0c7ea6ba9565b4684d7485f23c73aa53b00a164d9d5e703d15ae36","observation_id":"fd0ed772-13c0-415a-8943-8bf77f859e24","resolution":{"observed_at":"2026-08-05T17:45:11.587961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:11.331155Z","title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 2017","venue":null,"work_id":"79d47053-5356-4a6e-82c0-e2d2d81ce9ed","year":2017},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.282212Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:b7b0786eafbba1301c309ef152e387c9f8d2c0902ec4251a6c8ac632f5dbd8b3","observation_id":"c55847aa-ab56-4a47-8fdc-33eeb0e04d3b","resolution":{"observed_at":"2026-08-05T17:45:11.387270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:11.185966Z","title":"Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge.Nature Medicine, 28(1):154–163, 2022","venue":null,"work_id":"a53d0e86-b984-4373-a7ae-7b13b6b1c548","year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.316566Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:24cd22477db1f76f919551777cbfcb4971760df40909b04e5d06d1a9fb66c2e4","observation_id":"91e09d29-87b0-4a93-bd0c-d4844d931098","resolution":{"observed_at":"2026-08-05T17:45:11.240418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:11.035474Z","title":"Recent progress in the treatment of cancer in children.CA: a cancer journal for clinicians, 71(4):315–332, 2021","venue":null,"work_id":"b346dae3-d495-4810-b2f4-4a913c207ea1","year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.392262Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:4f85bfbb96e6734c7979688fbb372fcad276b85fdc8c30b386ae1ec1798f2675","observation_id":"f004cae4-a429-4923-8383-256361900f5a","resolution":{"observed_at":"2026-08-05T17:45:11.091412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:04.442190Z","title":"Emerging properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.442190Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:fc52f5433ed341527a3da691fbd7929940858646d41eefede7edeaec2f2aa47f","observation_id":"2968b93e-9f5a-466f-9b2f-a1e7ec00fce8","resolution":{"observed_at":"2026-08-05T17:45:04.442190Z","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":"2026-08-05T17:45:04.524670Z","title":"Towards a general-purpose foundation model for computational pathology.Nature Medicine, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.524670Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:744ddccfa50f92fa994450379750a3bad8ac9f99460e5a60b558534db3559aeb","observation_id":"8e304d61-cb05-4e20-8756-7e4cb8ec9537","resolution":{"observed_at":"2026-08-05T17:45:04.524670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04297","last_updated":"2020-03-09T17:56:49Z","snapshot_observed_at":"2026-07-06T09:03:25.467987Z","submitted_at":"2020-03-09T17:56:49Z","title":"Improved Baselines with Momentum Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04297","snapshot_observed_at":"2026-08-05T17:45:04.579941Z","title":"Improved baselines with momentum contrastive learning.arXiv preprint arXiv:2003.04297, 2020","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.579941Z"},"links":{"cited_paper":"/paper/2003.04297","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:db68f285836041396c579baafa95a57a1764e658f9c2a599913d5fc6eb863147","observation_id":"d684d948-e333-4f11-84f5-92759f7a8068","resolution":{"observed_at":"2026-08-05T17:45:04.579941Z","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":"2026-08-05T17:45:10.821088Z","title":"The burden of rare cancers in the united states","venue":null,"work_id":"717faf7c-6ac0-4177-a4c5-f2d1730cb15a","year":2017},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.626002Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:13ab75177d74c08c45bc8821a31c42b2fd515138bf533d511aab4880aa9dbd67","observation_id":"17c1b9c1-7b00-4a76-a502-45e8baaa2bbd","resolution":{"observed_at":"2026-08-05T17:45:10.893834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19666","last_updated":"2024-11-29T12:39:57Z","snapshot_observed_at":"2026-08-14T21:34:23.219534Z","submitted_at":"2024-11-29T12:39:57Z","title":"Multimodal Whole Slide Foundation Model for Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19666","snapshot_observed_at":"2026-08-05T17:45:04.707229Z","title":"Multimodal whole slide foundation model for pathology.arXiv preprint arXiv:2411.19666, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.707229Z"},"links":{"cited_paper":"/paper/2411.19666","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:1e74f9a8b346babcadf4e6c4595b6b0cdaf5e3e6dca002c76ce9b409d2a80a3f","observation_id":"b6a7cdf0-383d-4701-bf1f-54e7613314d9","resolution":{"observed_at":"2026-08-05T17:45:04.707229Z","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":"2026-08-05T17:45:10.633154Z","title":"Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.Modern Pathology, 35(12):1983–1990, 2022","venue":null,"work_id":"14d11cdc-06d7-4c30-830e-d323796449b0","year":1983},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.783567Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:2fba25ae48c0a880b0a26426f730a12109491043fb54ac126e2fcce96824a05e","observation_id":"dc1682c0-61d6-48a8-a59a-5a7adf7bcf92","resolution":{"observed_at":"2026-08-05T17:45:10.717917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:04.855088Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.855088Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:a222b5a0944c2467aa2501467f1305066cba149e0d626d7566ae001ce90befcb","observation_id":"2bdf7d73-539a-4deb-ba50-20e68e172406","resolution":{"observed_at":"2026-08-05T17:45:04.855088Z","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":"2026-08-05T17:45:04.946202Z","title":"A visual–language foundation model for pathology image analysis using medical twitter.Nature Medicine, 29(9):2307–2316, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:04.946202Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:dc4e814b44c1d5774adcb452eacbfb5fbcb3bffaa6a12795d11680793933c482","observation_id":"26dc0479-0a8b-4dcd-8c03-5571a79d0ca1","resolution":{"observed_at":"2026-08-05T17:45:04.946202Z","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":"2026-08-05T17:45:10.356689Z","title":"A comprehensive ai model development framework for consistent gleason grading.Communications Medicine, 4(1):84, 2024","venue":null,"work_id":"b6aec83a-6ecc-4e7e-a4d9-c798eaf7edfe","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.036718Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:12bfc60d1f680bde611c1ebee9b731c44bd76c19551df12cd324a8278a7faa70","observation_id":"5c3fb867-98d2-4d42-b63a-bb7e8a0137a6","resolution":{"observed_at":"2026-08-05T17:45:10.463456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:10.140215Z","title":"Quilt-1m: One million image-text pairs for histopathology.Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":"177c302c-4f2a-41c3-b56f-304c6603d1a5","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.123822Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:54e72705f380555a33cb672156ffc1614bc35a2ef014818252f2646df1e0a2aa","observation_id":"59848e5a-f638-449c-a52b-c6f59129ff38","resolution":{"observed_at":"2026-08-05T17:45:10.244696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04712","last_updated":"2018-06-28T13:33:03Z","snapshot_observed_at":"2026-08-14T19:46:18.439175Z","submitted_at":"2018-02-13T16:27:19Z","title":"Attention-based Deep Multiple Instance Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04712","snapshot_observed_at":"2026-08-05T17:45:05.215079Z","title":"Attention-based deep multiple instance learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.215079Z"},"links":{"cited_paper":"/paper/1802.04712","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:7d6db38ff64e9171b7cfc6dcc5d0e2d1016d6e4ce4f089c40dbc261b3d59242c","observation_id":"2117c4a6-5668-451a-9b07-3f7304a755ce","resolution":{"observed_at":"2026-08-05T17:45:05.215079Z","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":"2026-08-05T17:45:09.937505Z","title":"A visual-language foundation model for computational pathology.Nature Medicine, 30(3):863–874, 2024","venue":null,"work_id":"55eb61d9-47cb-4542-883f-d6cf6054546b","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.286325Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:5132683d886428c13d9f67d69985ae32fdeb77ce526b7cd8a06760e82258afb6","observation_id":"092b8f7e-dbeb-44c9-a63a-5429346474be","resolution":{"observed_at":"2026-08-05T17:45:10.072414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:09.725524Z","title":"Visual language pretrained multiple instance zero-shot transfer for histopathology images","venue":null,"work_id":"42ec868f-7427-4038-b1d5-e0442f0740e0","year":2023},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.357355Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:8843d7986e752b7a32eec70ea273f0207dfb3a157eea669cad8ccd471cae6015","observation_id":"6a29e17f-dc20-43c9-888e-4ce9a49757d2","resolution":{"observed_at":"2026-08-05T17:45:09.822069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:05.402964Z","title":"Data-efficient and weakly supervised computational pathology on whole-slide images.Nature Biomedical Engineering, 5(6):555–570, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.402964Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:f78c8299e5cf8435bbf019fcb2bda8f071cb16157634e566f1776792999b7712","observation_id":"80c0a022-1abf-4b47-90c1-e638bef59a78","resolution":{"observed_at":"2026-08-05T17:45:05.402964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18449","last_updated":"2025-04-14T09:55:21Z","snapshot_observed_at":"2026-08-15T02:49:46.919812Z","submitted_at":"2024-07-26T01:12:54Z","title":"Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.18449","snapshot_observed_at":"2026-08-05T17:45:05.490246Z","title":"Towards a generalizable pathology foundation model via unified knowledge distillation.arXiv preprint arXiv:2407.18449, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.490246Z"},"links":{"cited_paper":"/paper/2407.18449","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:a9299599f405041017263517f84aaf49962a4854c70c940d18758f507c8e1efc","observation_id":"cf06b662-94da-48ea-8019-810fbc4f1056","resolution":{"observed_at":"2026-08-05T17:45:05.490246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05074","last_updated":"2024-08-20T11:01:21Z","snapshot_observed_at":"2026-08-13T19:26:27.484885Z","submitted_at":"2024-06-07T16:45:53Z","title":"Hibou: A Family of Foundational Vision Transformers for Pathology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05074","snapshot_observed_at":"2026-08-05T17:45:05.561080Z","title":"Hibou: A family of foundational vision transformers for pathology.arXiv preprint arXiv:2406.05074, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.561080Z"},"links":{"cited_paper":"/paper/2406.05074","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:26b17ca620c569d18dcb878932a1af4b54dd1bdcafeca080abf6e9514f4f70da","observation_id":"08cfe245-d7d6-4ce4-b87e-76188b071caf","resolution":{"observed_at":"2026-08-05T17:45:05.561080Z","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":"2026-08-05T17:45:09.553999Z","title":"Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in china: a cross-sectional study.The Lancet, 400(10357):1020–1032, 2022","venue":null,"work_id":"a108968e-c832-438d-bb9a-1b8383b0093e","year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.632274Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:cd3432f57bced4df69a29232177c25bbfcbfd1904385e225bf6fc0af7e8b6565","observation_id":"dfdd1df1-d5c6-42d5-9a68-a8aa2ef3cbba","resolution":{"observed_at":"2026-08-05T17:45:09.595969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:09.402687Z","title":null,"venue":null,"work_id":"2a51896c-2362-48be-b2ac-d4847ac1eb51","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.684476Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:624473453972ece05eb85209f905c92fffa9a7cabc29246b512b9ff440992a56","observation_id":"7a603d91-4561-48b1-acd1-6263a9ad47f7","resolution":{"observed_at":"2026-08-05T17:45:09.468248Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:05.753186Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.753186Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:1fc5d5b8c6db54b0576c962e8971989761479423f14d8986b07b38113d5a8b5b","observation_id":"ab931f05-8a9b-43c5-8ff2-072330a4cbe2","resolution":{"observed_at":"2026-08-05T17:45:05.753186Z","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":"2026-08-05T17:45:09.161856Z","title":"The digital brain tumour atlas, an open histopathology resource.Scientific Data, 9(1):55, 2022","venue":null,"work_id":"36fb76fd-f89a-4b44-a6b4-77844f86400f","year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.838238Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:4ce2e1974e180c8834436e92a3e7e44653d39ba3535c3f668bfe88fd477f4856","observation_id":"e8058d27-ad50-4259-98a3-00f43a8574f6","resolution":{"observed_at":"2026-08-05T17:45:09.266494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10254","last_updated":"2024-05-22T17:22:32Z","snapshot_observed_at":"2026-08-14T07:50:43.939149Z","submitted_at":"2024-05-16T16:59:12Z","title":"PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10254","snapshot_observed_at":"2026-08-05T17:45:05.887607Z","title":"Prism: A multi-modal generative foundation model for slide-level histopathology.arXiv preprint arXiv:2405.10254, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.887607Z"},"links":{"cited_paper":"/paper/2405.10254","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:314f53325801d2d6baa46ff2e36b187e6f79ec966906ef84d9dbe6704847de15","observation_id":"9d1db609-3105-4e1d-8e1d-efcb3bedeec8","resolution":{"observed_at":"2026-08-05T17:45:05.887607Z","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":"2026-08-05T17:45:09.005355Z","title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in Neural Information Processing Systems, 34:2136–2147, 2021","venue":null,"work_id":"a0381f54-b992-4e33-8b7e-f65cbdb92a30","year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:05.963854Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:132111007dc245cf5167e67d13ffdc8f6791ad5210588b4fa0e77c0143ac5e54","observation_id":"59521e8b-a84d-4b35-8df6-2f2a951d0a85","resolution":{"observed_at":"2026-08-05T17:45:09.076704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:08.837014Z","title":"ViLa-MIL: Dual-scale vision- language multiple instance learning for whole slide image classification","venue":null,"work_id":"d5ac0a80-247f-4f1a-acdf-702374553c10","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.017861Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:339406e108e4bba1acbd98a44dd9ea4fef9b7871ce8f5194cbfafc6233846160","observation_id":"403d775d-b326-46f5-b660-699eaf23756a","resolution":{"observed_at":"2026-08-05T17:45:08.920510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:08.684278Z","title":"Cpath-omni: A unified multimodal foundation model for patch and whole slide image analysis in computational pathology","venue":null,"work_id":"fa1270c3-fd9e-4bd7-b867-f2e437eb18fd","year":2025},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.082479Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:5e8b56916fc68114203fa4c76d150a6ac91d0e79289a534c7db0df7f19fd2478","observation_id":"dcea8e5e-3c62-46b0-b929-7c2cfa56339b","resolution":{"observed_at":"2026-08-05T17:45:08.761834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:08.497393Z","title":"A foundation model for clinical-grade computational pathology and rare cancers detection.Nature Medicine, pages 1–12, 2024","venue":null,"work_id":"5d0fc284-0992-4861-bd85-b0ec643c541b","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.161098Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:dc1d283475b80b558fe43c777748b66ce2df8e63669aa78ab43a95d1998fa904","observation_id":"1bccf111-35cc-41e0-8e7e-33687ee1b095","resolution":{"observed_at":"2026-08-05T17:45:08.582737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:08.338735Z","title":"Transformer-based unsupervised contrastive learning for histopathological image classification","venue":null,"work_id":"f0291189-ca43-4207-87be-0644f5382b0e","year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.214608Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:c6c9e32eb9aae60418a7b7c07ac24333141da0e478d5056c5a34868c25aa0e2f","observation_id":"c2142359-b611-42bb-8ae0-4b911dabe50a","resolution":{"observed_at":"2026-08-05T17:45:08.407612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:08.152527Z","title":"A vision–language foundation model for precision oncology.Nature, pages 1–10, 2025","venue":null,"work_id":"bc01f825-4301-422c-85da-4bf9643d42fe","year":2025},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.264723Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:52e547384ca09e735a45634f6f06db6970d9b6e64b4c93e079ea601856b144bd","observation_id":"ae866e7f-a013-49fa-a88f-55617727f0e0","resolution":{"observed_at":"2026-08-05T17:45:08.282033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:07.987339Z","title":"A whole-slide foundation model for digital pathology from real-world data.Nature, pages 1–8, 2024","venue":null,"work_id":"204232d3-8995-4755-82a5-f2bb83f64b97","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.347740Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:a976ebb8f90a9efd4f503b382e20aae049dad1b15cb009e77d967a39298bdf0f","observation_id":"f783806c-301a-4093-9e3c-80ae6a862b8f","resolution":{"observed_at":"2026-08-05T17:45:08.062356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15362","last_updated":"2025-03-25T08:49:58Z","snapshot_observed_at":"2026-08-14T12:38:43.741825Z","submitted_at":"2024-07-22T04:09:27Z","title":"A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15362","snapshot_observed_at":"2026-08-05T17:45:06.449206Z","title":"A multimodal knowledge-enhanced whole-slide pathology foundation model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.449206Z"},"links":{"cited_paper":"/paper/2407.15362","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:30c3475f7ce1fe6ed14b5dd19c36be6abea596db28b079cbc200aa2f37ecf42d","observation_id":"5de9e9b4-3b3b-4b0b-8a74-0ee4b74e9fff","resolution":{"observed_at":"2026-08-05T17:45:06.449206Z","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":"2026-08-05T17:45:07.850937Z","title":"A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images.Nature Communications, 16(1):2366, 2025","venue":null,"work_id":"820b111c-275c-4619-85bc-087acd26866b","year":2025},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.538964Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:775a758f65993fb09f9fcdfd2888ae1d9b682e2f4b4e607062c86755f2b89c9f","observation_id":"660865bc-3870-4637-82e1-e5397cd4c582","resolution":{"observed_at":"2026-08-05T17:45:07.905322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01917","last_updated":"2022-06-14T00:48:04Z","snapshot_observed_at":"2026-08-13T15:12:42.567441Z","submitted_at":"2022-05-04T07:01:14Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01917","snapshot_observed_at":"2026-08-05T17:45:06.559306Z","title":"Coca: Contrastive captioners are image-text foundation models.arXiv preprint arXiv:2205.01917, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.559306Z"},"links":{"cited_paper":"/paper/2205.01917","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:1d02855391b1a0d2430aee874ef868b776ce5c4b634068d44ea2232e8a693148","observation_id":"3b067170-259f-4178-a1d2-ead87b02c3ec","resolution":{"observed_at":"2026-08-05T17:45:06.559306Z","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":"2026-08-05T17:45:06.564037Z","title":"Sigmoid loss for language image pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.564037Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:e9033589e71511382325ea7011b22439eb3a4c96cec45bea7cb570c9fb19985e","observation_id":"2cf21eef-0e8a-4a3e-8347-74b15b146dbe","resolution":{"observed_at":"2026-08-05T17:45:06.564037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07832","last_updated":"2022-01-27T09:20:49Z","snapshot_observed_at":"2026-07-06T12:08:39.149450Z","submitted_at":"2021-11-15T15:18:05Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07832","snapshot_observed_at":"2026-08-05T17:45:06.636212Z","title":"ibot: Image bert pre-training with online tokenizer.arXiv preprint arXiv:2111.07832, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.636212Z"},"links":{"cited_paper":"/paper/2111.07832","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:bcbb0ad5a9111d406bfb032bd065ce793b647e9e0cd1580cf07a6e7dcba3e945","observation_id":"e9a2fd5a-f921-4e50-abd7-acda6cd3b85b","resolution":{"observed_at":"2026-08-05T17:45:06.636212Z","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":"2026-08-05T17:45:06.792677Z","title":"Learning to prompt for vision-language models.International Journal of Computer Vision (IJCV), 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.792677Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:93622bde48357b62647af2239ce9bb22b47c3a0e8ec22218e9b0b41217f4b223","observation_id":"31738ac7-5f64-43a9-a111-0ed791619111","resolution":{"observed_at":"2026-08-05T17:45:06.792677Z","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":"2412.13126","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T17:45:07.411187Z","title":"A knowledge-enhanced pathology vision-language foundation model for cancer diagnosis.arXiv preprint arXiv:2412.13126, 2024","venue":null,"work_id":"29b9e30f-bb52-4f2a-b3cb-fcce9903b9ee","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.885582Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:67459638cbb1e2e52986d5718fd2d0f48f174314c51875d95c06ec8c8242404c","observation_id":"0e00befc-e780-4fd3-b7b3-d86c605f2296","resolution":{"observed_at":"2026-08-05T17:45:07.484448Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T17:45:07.664125Z","title":"Knowledge-enhanced visual-language pretraining for computational pathology","venue":null,"work_id":"d33cc6ba-3d8f-4f88-a643-77f5e2027a9b","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:06.987999Z"},"links":{"citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:95a970e691b323836187bc83d2206d296207ca72e5befb02fc9e72b5389cc8f3","observation_id":"a3cffbb0-f5d4-40c3-8d0a-c9bca2c55729","resolution":{"observed_at":"2026-08-05T17:45:07.736462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03575","last_updated":"2024-07-04T01:58:30Z","snapshot_observed_at":"2026-08-12T23:29:12.015725Z","submitted_at":"2024-07-04T01:58:30Z","title":"DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification","version":1},"cited_work":{"arxiv_id":"2407.03575","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.03575","snapshot_observed_at":"2026-08-05T17:45:07.181056Z","title":"DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification","venue":"eess.IV","work_id":"005ba9fa-a5d3-4dbe-8915-fa870d26856c","year":2024},"citing_paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T17:45:07.090571Z"},"links":{"cited_paper":"/paper/2407.03575","citing_paper":"/paper/2508.15904"},"observation_digest":"sha256:eaa8a420fe6fe253247bf4381780951b67e1d693a100e904830cd76c778a8635","observation_id":"f7ac9f65-0058-405a-90ca-6304bd88f9e4","resolution":{"observed_at":"2026-08-05T17:45:07.258164Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.15904","last_updated":"2025-08-21T18:04:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T12:13:57.900930Z","submitted_at":"2025-08-21T18:04:41Z","title":"Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":2,"verified_fuzzy":21},"total_outbound_references":41},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2508.15904."}