{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PWQ4C2VPCHQQ4QPPN3KS7IXONC","short_pith_number":"pith:PWQ4C2VP","schema_version":"1.0","canonical_sha256":"7da1c16aaf11e10e41ef6ed52fa2ee68869c6374fb5cccf014e0286d9ff32839","source":{"kind":"arxiv","id":"2510.12953","version":4},"attestation_state":"computed","paper":{"title":"Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Bo Du, Dacheng Tao, Guojia Wan, Huangxuan Zhao, Jiancheng Pan, Juhua Liu, Wei Zhou, Xiao He, Yanxing Liu, Yongchao Xu, Yong Luo","submitted_at":"2025-10-14T19:57:03Z","abstract_excerpt":"Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are adapted to structured adult imaging and underperform in fetal ultrasound, which poses challenges of multi-view image reasoning, numerous diseases, and image diversity. To bridge this gap, we introduce FetalMind, a medical AI system tailored to fetal ultrasound for both report generation and diagnosis. Guided by clinical workflow, we propose Salient Epistemic Disentanglement (SED), which injects an expert-curated bipartite graph into the model to decouple vi"},"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":"2510.12953","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-10-14T19:57:03Z","cross_cats_sorted":["cs.AI","cs.IR","cs.MM"],"title_canon_sha256":"4473c812ac323e5089eeba86945a5c070c8250dcc0e2814f507d1abbc7dda819","abstract_canon_sha256":"315e099e8d8c199198315b0fe2d8978fa21bc11d288800b4c171c874ddd0a5c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T01:11:59.052189Z","signature_b64":"JM36MEqByLUbSPQ5BNo16sPt28ng2hW24OmIcIJOOslkbC31ABff48zmlP1Ta2ykJchVUq8fst0ux2wKfI1WBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7da1c16aaf11e10e41ef6ed52fa2ee68869c6374fb5cccf014e0286d9ff32839","last_reissued_at":"2026-08-03T01:11:59.050499Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T01:11:59.050499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Bo Du, Dacheng Tao, Guojia Wan, Huangxuan Zhao, Jiancheng Pan, Juhua Liu, Wei Zhou, Xiao He, Yanxing Liu, Yongchao Xu, Yong Luo","submitted_at":"2025-10-14T19:57:03Z","abstract_excerpt":"Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are adapted to structured adult imaging and underperform in fetal ultrasound, which poses challenges of multi-view image reasoning, numerous diseases, and image diversity. To bridge this gap, we introduce FetalMind, a medical AI system tailored to fetal ultrasound for both report generation and diagnosis. Guided by clinical workflow, we propose Salient Epistemic Disentanglement (SED), which injects an expert-curated bipartite graph into the model to decouple vi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.12953","kind":"arxiv","version":4},"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/2510.12953/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":"2510.12953","created_at":"2026-08-03T01:11:59.051534+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.12953v4","created_at":"2026-08-03T01:11:59.051534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.12953","created_at":"2026-08-03T01:11:59.051534+00:00"},{"alias_kind":"pith_short_12","alias_value":"PWQ4C2VPCHQQ","created_at":"2026-08-03T01:11:59.051534+00:00"},{"alias_kind":"pith_short_16","alias_value":"PWQ4C2VPCHQQ4QPP","created_at":"2026-08-03T01:11:59.051534+00:00"},{"alias_kind":"pith_short_8","alias_value":"PWQ4C2VP","created_at":"2026-08-03T01:11:59.051534+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.11106","citing_title":"FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC","json":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC.json","graph_json":"https://pith.science/api/pith-number/PWQ4C2VPCHQQ4QPPN3KS7IXONC/graph.json","events_json":"https://pith.science/api/pith-number/PWQ4C2VPCHQQ4QPPN3KS7IXONC/events.json","paper":"https://pith.science/paper/PWQ4C2VP"},"agent_actions":{"view_html":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC","download_json":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC.json","view_paper":"https://pith.science/paper/PWQ4C2VP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.12953&json=true","fetch_graph":"https://pith.science/api/pith-number/PWQ4C2VPCHQQ4QPPN3KS7IXONC/graph.json","fetch_events":"https://pith.science/api/pith-number/PWQ4C2VPCHQQ4QPPN3KS7IXONC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC/action/storage_attestation","attest_author":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC/action/author_attestation","sign_citation":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC/action/citation_signature","submit_replication":"https://pith.science/pith/PWQ4C2VPCHQQ4QPPN3KS7IXONC/action/replication_record"}},"created_at":"2026-08-03T01:11:59.051534+00:00","updated_at":"2026-08-03T01:11:59.051534+00:00"}