{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7UVT3THUCY3Q4DBG6VPQON7GJ5","short_pith_number":"pith:7UVT3THU","schema_version":"1.0","canonical_sha256":"fd2b3dccf416370e0c26f55f0737e64f7d2d5f8043cf278d359c5852b63100ae","source":{"kind":"arxiv","id":"2501.15724","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajit J. Nirmal, Chen Zhao, Christine G. Lian, Dong Li, Guihong Wan, Peter K. Sorger, Xintao Wu, Xinyu Wu, Yevgeniy R. Semenov","submitted_at":"2025-01-27T01:27:59Z","abstract_excerpt":"Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.15724","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-27T01:27:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cef2963b2bea8575e004f1cc47543f8a235b6dd9c08a3b95c5a92cb3b25160f5","abstract_canon_sha256":"96e01f84791d792cd7c102b9e62a63dc9e930e517099294c529bc063f4b574b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:57.153221Z","signature_b64":"8OCioblPw8OjbnioUF67h8BeUwXy9qwufiWjhMCXfdjUvJKlhT85t0xpFpTakOZzIdpsKVkBc8fOk996ygN1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd2b3dccf416370e0c26f55f0737e64f7d2d5f8043cf278d359c5852b63100ae","last_reissued_at":"2026-07-05T10:19:57.152730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:57.152730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajit J. Nirmal, Chen Zhao, Christine G. Lian, Dong Li, Guihong Wan, Peter K. Sorger, Xintao Wu, Xinyu Wu, Yevgeniy R. Semenov","submitted_at":"2025-01-27T01:27:59Z","abstract_excerpt":"Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15724","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.15724/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.15724","created_at":"2026-07-05T10:19:57.152788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15724v2","created_at":"2026-07-05T10:19:57.152788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15724","created_at":"2026-07-05T10:19:57.152788+00:00"},{"alias_kind":"pith_short_12","alias_value":"7UVT3THUCY3Q","created_at":"2026-07-05T10:19:57.152788+00:00"},{"alias_kind":"pith_short_16","alias_value":"7UVT3THUCY3Q4DBG","created_at":"2026-07-05T10:19:57.152788+00:00"},{"alias_kind":"pith_short_8","alias_value":"7UVT3THU","created_at":"2026-07-05T10:19:57.152788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20677","citing_title":"Democratizing and accelerating AI-driven pathology research through agentic intelligence","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07590","citing_title":"SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2510.23807","citing_title":"Beyond the Failures: Rethinking Foundation Models in Pathology","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24679","citing_title":"Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5","json":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5.json","graph_json":"https://pith.science/api/pith-number/7UVT3THUCY3Q4DBG6VPQON7GJ5/graph.json","events_json":"https://pith.science/api/pith-number/7UVT3THUCY3Q4DBG6VPQON7GJ5/events.json","paper":"https://pith.science/paper/7UVT3THU"},"agent_actions":{"view_html":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5","download_json":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5.json","view_paper":"https://pith.science/paper/7UVT3THU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15724&json=true","fetch_graph":"https://pith.science/api/pith-number/7UVT3THUCY3Q4DBG6VPQON7GJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/7UVT3THUCY3Q4DBG6VPQON7GJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5/action/storage_attestation","attest_author":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5/action/author_attestation","sign_citation":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5/action/citation_signature","submit_replication":"https://pith.science/pith/7UVT3THUCY3Q4DBG6VPQON7GJ5/action/replication_record"}},"created_at":"2026-07-05T10:19:57.152788+00:00","updated_at":"2026-07-05T10:19:57.152788+00:00"}