{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BDZ2H4BVMWQRFODUJFLC2V727T","short_pith_number":"pith:BDZ2H4BV","schema_version":"1.0","canonical_sha256":"08f3a3f03565a112b87449562d57fafcd5bdb727e4e644dda4759399f754d2b6","source":{"kind":"arxiv","id":"2605.31093","version":1},"attestation_state":"computed","paper":{"title":"Cross-Modal Clinical Knowledge Integration for Mammography Report Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuxiang Huang, Hao Chen, Jiayi Zhu, Qingcong Kong, Qiong Luo, Xi Wang, Yuan Guo, Yu Xie, Zhenhui Li, Zhixuan Chen","submitted_at":"2026-05-29T10:04:16Z","abstract_excerpt":"Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examinations creates a substantial workload for radiologists, making accurate and consistent report generation a critical clinical challenge. Existing automated mammography report generation methods primarily focus on direct visual-to-text mapping, while overlooking the structured clinical reasoning process followed by radiologists in real-world practice. To address this limitation, we propose MammoRG, a mammography report generation framework that ex"},"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":"2605.31093","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T10:04:16Z","cross_cats_sorted":[],"title_canon_sha256":"d7a81baf576cb27f4e0bce9b746d725a623c99b77613e4bb50f73ca8364f5adb","abstract_canon_sha256":"8bcd95867fa05d3ba107381bf21548301965d6119b9ca235de068d69e4f78f84"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:57.545198Z","signature_b64":"6VO2vyndbWl0r6DYpjKR/d+5bgU05Qe/BNG/NY8Lcc5GPge4kFPVJ0hcrxJuycz0i0TsfPqor/AnDC/ZakolBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08f3a3f03565a112b87449562d57fafcd5bdb727e4e644dda4759399f754d2b6","last_reissued_at":"2026-06-01T01:03:57.544436Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:57.544436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-Modal Clinical Knowledge Integration for Mammography Report Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuxiang Huang, Hao Chen, Jiayi Zhu, Qingcong Kong, Qiong Luo, Xi Wang, Yuan Guo, Yu Xie, Zhenhui Li, Zhixuan Chen","submitted_at":"2026-05-29T10:04:16Z","abstract_excerpt":"Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examinations creates a substantial workload for radiologists, making accurate and consistent report generation a critical clinical challenge. Existing automated mammography report generation methods primarily focus on direct visual-to-text mapping, while overlooking the structured clinical reasoning process followed by radiologists in real-world practice. To address this limitation, we propose MammoRG, a mammography report generation framework that ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31093","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/2605.31093/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":"2605.31093","created_at":"2026-06-01T01:03:57.544556+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31093v1","created_at":"2026-06-01T01:03:57.544556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31093","created_at":"2026-06-01T01:03:57.544556+00:00"},{"alias_kind":"pith_short_12","alias_value":"BDZ2H4BVMWQR","created_at":"2026-06-01T01:03:57.544556+00:00"},{"alias_kind":"pith_short_16","alias_value":"BDZ2H4BVMWQRFODU","created_at":"2026-06-01T01:03:57.544556+00:00"},{"alias_kind":"pith_short_8","alias_value":"BDZ2H4BV","created_at":"2026-06-01T01:03:57.544556+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/BDZ2H4BVMWQRFODUJFLC2V727T","json":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T.json","graph_json":"https://pith.science/api/pith-number/BDZ2H4BVMWQRFODUJFLC2V727T/graph.json","events_json":"https://pith.science/api/pith-number/BDZ2H4BVMWQRFODUJFLC2V727T/events.json","paper":"https://pith.science/paper/BDZ2H4BV"},"agent_actions":{"view_html":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T","download_json":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T.json","view_paper":"https://pith.science/paper/BDZ2H4BV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31093&json=true","fetch_graph":"https://pith.science/api/pith-number/BDZ2H4BVMWQRFODUJFLC2V727T/graph.json","fetch_events":"https://pith.science/api/pith-number/BDZ2H4BVMWQRFODUJFLC2V727T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T/action/storage_attestation","attest_author":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T/action/author_attestation","sign_citation":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T/action/citation_signature","submit_replication":"https://pith.science/pith/BDZ2H4BVMWQRFODUJFLC2V727T/action/replication_record"}},"created_at":"2026-06-01T01:03:57.544556+00:00","updated_at":"2026-06-01T01:03:57.544556+00:00"}