{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MVEOZG4UTOO5PAMEVBDEXQ3YEA","short_pith_number":"pith:MVEOZG4U","schema_version":"1.0","canonical_sha256":"6548ec9b949b9dd78184a8464bc3782011215f12b3b77d9a01857a19a5b2ed21","source":{"kind":"arxiv","id":"2310.07033","version":1},"attestation_state":"computed","paper":{"title":"Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Adam Schoenfeld, Alexandros D. Polydorides, Aryeh Stock, Brandon Veremis, Carlos Cordon-Cardo, Chad Vanderbilt, Cyrus Hedvat, Eugene Fluder, Gabriele Campanella, Jennifer Zeng, Patricia Kovatch, Ricky Kwan, Thomas J. Fuchs","submitted_at":"2023-10-10T21:40:19Z","abstract_excerpt":"Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology, has not been extensively studied. Previous work in self-supervised learning in pathology has leveraged smaller datasets for both pre-training and evaluating downstream performance. The aim of this project is to train the largest academic fo"},"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":"2310.07033","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-10T21:40:19Z","cross_cats_sorted":["cs.AI","cs.LG","eess.IV"],"title_canon_sha256":"438470d20afc790f6f886b886e1ed8faf8fc3d1a6291ae77d07d9a2534522986","abstract_canon_sha256":"b3372b8a2338fd9a4b5b34f0b1db3909e6ff22fb242c708019469172ae2595c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:39.907866Z","signature_b64":"OuDzTPQtdlz+Fvw/tWY/zTUdwyD/MhmtqT4UZZ9DKN1o6lf2XMWbiqnoFY980mm8f6f2X941obiQKMuan8AaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6548ec9b949b9dd78184a8464bc3782011215f12b3b77d9a01857a19a5b2ed21","last_reissued_at":"2026-07-05T06:59:39.907460Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:39.907460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Adam Schoenfeld, Alexandros D. Polydorides, Aryeh Stock, Brandon Veremis, Carlos Cordon-Cardo, Chad Vanderbilt, Cyrus Hedvat, Eugene Fluder, Gabriele Campanella, Jennifer Zeng, Patricia Kovatch, Ricky Kwan, Thomas J. Fuchs","submitted_at":"2023-10-10T21:40:19Z","abstract_excerpt":"Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology, has not been extensively studied. Previous work in self-supervised learning in pathology has leveraged smaller datasets for both pre-training and evaluating downstream performance. The aim of this project is to train the largest academic fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.07033","kind":"arxiv","version":1},"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/2310.07033/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":"2310.07033","created_at":"2026-07-05T06:59:39.907517+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.07033v1","created_at":"2026-07-05T06:59:39.907517+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.07033","created_at":"2026-07-05T06:59:39.907517+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVEOZG4UTOO5","created_at":"2026-07-05T06:59:39.907517+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVEOZG4UTOO5PAME","created_at":"2026-07-05T06:59:39.907517+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVEOZG4U","created_at":"2026-07-05T06:59:39.907517+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA","json":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA.json","graph_json":"https://pith.science/api/pith-number/MVEOZG4UTOO5PAMEVBDEXQ3YEA/graph.json","events_json":"https://pith.science/api/pith-number/MVEOZG4UTOO5PAMEVBDEXQ3YEA/events.json","paper":"https://pith.science/paper/MVEOZG4U"},"agent_actions":{"view_html":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA","download_json":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA.json","view_paper":"https://pith.science/paper/MVEOZG4U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.07033&json=true","fetch_graph":"https://pith.science/api/pith-number/MVEOZG4UTOO5PAMEVBDEXQ3YEA/graph.json","fetch_events":"https://pith.science/api/pith-number/MVEOZG4UTOO5PAMEVBDEXQ3YEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA/action/storage_attestation","attest_author":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA/action/author_attestation","sign_citation":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA/action/citation_signature","submit_replication":"https://pith.science/pith/MVEOZG4UTOO5PAMEVBDEXQ3YEA/action/replication_record"}},"created_at":"2026-07-05T06:59:39.907517+00:00","updated_at":"2026-07-05T06:59:39.907517+00:00"}