{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2LAICRK7IUDEIBNB663AKMROWE","short_pith_number":"pith:2LAICRK7","schema_version":"1.0","canonical_sha256":"d2c081455f45064405a1f7b605322eb1270826715f78edaf0fcc7e308ca5b69d","source":{"kind":"arxiv","id":"2411.11458","version":2},"attestation_state":"computed","paper":{"title":"HistoEncoder: a digital pathology foundation model for prostate cancer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Abderrahim-Oussama Batouche, Andrew Erickson, Antti Rannikko, Esa Pitkanen, Joona Pohjonen, Kevin Sandeman, Tuomas Mirtti","submitted_at":"2024-11-18T10:46:05Z","abstract_excerpt":"Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that HistoEncoder features extracted from tile images with similar histological patterns map closely together in the feature space. HistoEncoder outperforms models pre-trained with natural images, even without fine-tuning or with 1000 times less trai"},"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":"2411.11458","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-18T10:46:05Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"bf5b3f4a1b1c526bac928e05526fd5d480b89e058d741e1f8b3697e1c0ec16ee","abstract_canon_sha256":"8054a29dcbe48eef4b3cdec33a54b2b52d11a7aa81ee40f88ed5153807391e5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:10.384542Z","signature_b64":"zSy3ejhg9bKo8Z8AWM5OzCtGalwA9Txq+PozE0n9g1Ft2AEFwSOHjdxwAp1x8AtlyxcFVPMQKp1ICFUM8NEoDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2c081455f45064405a1f7b605322eb1270826715f78edaf0fcc7e308ca5b69d","last_reissued_at":"2026-07-05T09:39:10.384074Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:10.384074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HistoEncoder: a digital pathology foundation model for prostate cancer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Abderrahim-Oussama Batouche, Andrew Erickson, Antti Rannikko, Esa Pitkanen, Joona Pohjonen, Kevin Sandeman, Tuomas Mirtti","submitted_at":"2024-11-18T10:46:05Z","abstract_excerpt":"Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that HistoEncoder features extracted from tile images with similar histological patterns map closely together in the feature space. HistoEncoder outperforms models pre-trained with natural images, even without fine-tuning or with 1000 times less trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11458","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/2411.11458/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":"2411.11458","created_at":"2026-07-05T09:39:10.384131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.11458v2","created_at":"2026-07-05T09:39:10.384131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11458","created_at":"2026-07-05T09:39:10.384131+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LAICRK7IUDE","created_at":"2026-07-05T09:39:10.384131+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LAICRK7IUDEIBNB","created_at":"2026-07-05T09:39:10.384131+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LAICRK7","created_at":"2026-07-05T09:39:10.384131+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/2LAICRK7IUDEIBNB663AKMROWE","json":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE.json","graph_json":"https://pith.science/api/pith-number/2LAICRK7IUDEIBNB663AKMROWE/graph.json","events_json":"https://pith.science/api/pith-number/2LAICRK7IUDEIBNB663AKMROWE/events.json","paper":"https://pith.science/paper/2LAICRK7"},"agent_actions":{"view_html":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE","download_json":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE.json","view_paper":"https://pith.science/paper/2LAICRK7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.11458&json=true","fetch_graph":"https://pith.science/api/pith-number/2LAICRK7IUDEIBNB663AKMROWE/graph.json","fetch_events":"https://pith.science/api/pith-number/2LAICRK7IUDEIBNB663AKMROWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE/action/storage_attestation","attest_author":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE/action/author_attestation","sign_citation":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE/action/citation_signature","submit_replication":"https://pith.science/pith/2LAICRK7IUDEIBNB663AKMROWE/action/replication_record"}},"created_at":"2026-07-05T09:39:10.384131+00:00","updated_at":"2026-07-05T09:39:10.384131+00:00"}