{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6SCDVATULU2PYIRHU6YX2NRTGS","short_pith_number":"pith:6SCDVATU","schema_version":"1.0","canonical_sha256":"f4843a82745d34fc2227a7b17d363334ad7dbf265cab56df65ccd12b52718690","source":{"kind":"arxiv","id":"2408.09530","version":1},"attestation_state":"computed","paper":{"title":"PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dawei Dai, Guoyin Wang, Long Xu, Qianlan Yang, Shuyin Xia, Xiaojing Shen, Yuanhui Zhang","submitted_at":"2024-08-18T16:30:32Z","abstract_excerpt":"The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies has demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domain-specific alignment; (2) Using the proposed image-text data, we "},"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":"2408.09530","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-08-18T16:30:32Z","cross_cats_sorted":[],"title_canon_sha256":"37e437dfb6ddbfa3340b85bc4969cb4f4e8dbd78780eadb867130831e41bfd71","abstract_canon_sha256":"6e2905b858cc73df2c667b7bb707d5ef066d5b30f79eb27eb821b4443aac3f1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:33.881449Z","signature_b64":"W/Tsx4JXJZGnaEjJXLX8xAIr5cSyFNUCd3A/5/w5UW7qSokwpGxAGav5NXwg/k4CKHSaBR2CReuqwqxzfr7LAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4843a82745d34fc2227a7b17d363334ad7dbf265cab56df65ccd12b52718690","last_reissued_at":"2026-07-05T08:56:33.881019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:33.881019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dawei Dai, Guoyin Wang, Long Xu, Qianlan Yang, Shuyin Xia, Xiaojing Shen, Yuanhui Zhang","submitted_at":"2024-08-18T16:30:32Z","abstract_excerpt":"The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies has demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domain-specific alignment; (2) Using the proposed image-text data, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.09530","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/2408.09530/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":"2408.09530","created_at":"2026-07-05T08:56:33.881076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.09530v1","created_at":"2026-07-05T08:56:33.881076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.09530","created_at":"2026-07-05T08:56:33.881076+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SCDVATULU2P","created_at":"2026-07-05T08:56:33.881076+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SCDVATULU2PYIRH","created_at":"2026-07-05T08:56:33.881076+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SCDVATU","created_at":"2026-07-05T08:56:33.881076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09209","citing_title":"Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS","json":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS.json","graph_json":"https://pith.science/api/pith-number/6SCDVATULU2PYIRHU6YX2NRTGS/graph.json","events_json":"https://pith.science/api/pith-number/6SCDVATULU2PYIRHU6YX2NRTGS/events.json","paper":"https://pith.science/paper/6SCDVATU"},"agent_actions":{"view_html":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS","download_json":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS.json","view_paper":"https://pith.science/paper/6SCDVATU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.09530&json=true","fetch_graph":"https://pith.science/api/pith-number/6SCDVATULU2PYIRHU6YX2NRTGS/graph.json","fetch_events":"https://pith.science/api/pith-number/6SCDVATULU2PYIRHU6YX2NRTGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS/action/storage_attestation","attest_author":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS/action/author_attestation","sign_citation":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS/action/citation_signature","submit_replication":"https://pith.science/pith/6SCDVATULU2PYIRHU6YX2NRTGS/action/replication_record"}},"created_at":"2026-07-05T08:56:33.881076+00:00","updated_at":"2026-07-05T08:56:33.881076+00:00"}