{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NBKKYVLJY2LDRB6VUWYCLRTYRK","short_pith_number":"pith:NBKKYVLJ","schema_version":"1.0","canonical_sha256":"6854ac5569c6963887d5a5b025c6788a9a9a5dc4e0793ebe30e805fa8c9e27c2","source":{"kind":"arxiv","id":"2411.18101","version":1},"attestation_state":"computed","paper":{"title":"Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lequan Yu, Maximus Yeung, Weiqin Zhao, Yinshuang Fan, Yuming Jiang, Ziyu Guo","submitted_at":"2024-11-27T07:27:52Z","abstract_excerpt":"Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a novel knowledge concept-based MIL framework, named ConcepPath to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable diseasespecific human expert concepts from medical literature, and incorporate them with a group of pu"},"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":"2411.18101","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T07:27:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"92e5d72a126b787e3e4bd31cad329a8ba08af4dc13007aa0b8e61ff6b70e7c7a","abstract_canon_sha256":"bbbc246b3260c5348fca8851f0b203d67b1a116bcf76284d952ef8181edfdfa0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:11.864722Z","signature_b64":"bA/Z3p21r7uLGpf3R9HblDiOnY6OxVztGkmkVssLCSuQBwJv0cZ9wXE/GMNKNLaD+zzLD1i8DwnvXwIRXZXiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6854ac5569c6963887d5a5b025c6788a9a9a5dc4e0793ebe30e805fa8c9e27c2","last_reissued_at":"2026-07-05T09:41:11.864288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:11.864288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Lequan Yu, Maximus Yeung, Weiqin Zhao, Yinshuang Fan, Yuming Jiang, Ziyu Guo","submitted_at":"2024-11-27T07:27:52Z","abstract_excerpt":"Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a novel knowledge concept-based MIL framework, named ConcepPath to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable diseasespecific human expert concepts from medical literature, and incorporate them with a group of pu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.18101","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/2411.18101/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":"2411.18101","created_at":"2026-07-05T09:41:11.864363+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.18101v1","created_at":"2026-07-05T09:41:11.864363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.18101","created_at":"2026-07-05T09:41:11.864363+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBKKYVLJY2LD","created_at":"2026-07-05T09:41:11.864363+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBKKYVLJY2LDRB6V","created_at":"2026-07-05T09:41:11.864363+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBKKYVLJ","created_at":"2026-07-05T09:41:11.864363+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/NBKKYVLJY2LDRB6VUWYCLRTYRK","json":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK.json","graph_json":"https://pith.science/api/pith-number/NBKKYVLJY2LDRB6VUWYCLRTYRK/graph.json","events_json":"https://pith.science/api/pith-number/NBKKYVLJY2LDRB6VUWYCLRTYRK/events.json","paper":"https://pith.science/paper/NBKKYVLJ"},"agent_actions":{"view_html":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK","download_json":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK.json","view_paper":"https://pith.science/paper/NBKKYVLJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.18101&json=true","fetch_graph":"https://pith.science/api/pith-number/NBKKYVLJY2LDRB6VUWYCLRTYRK/graph.json","fetch_events":"https://pith.science/api/pith-number/NBKKYVLJY2LDRB6VUWYCLRTYRK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK/action/storage_attestation","attest_author":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK/action/author_attestation","sign_citation":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK/action/citation_signature","submit_replication":"https://pith.science/pith/NBKKYVLJY2LDRB6VUWYCLRTYRK/action/replication_record"}},"created_at":"2026-07-05T09:41:11.864363+00:00","updated_at":"2026-07-05T09:41:11.864363+00:00"}