{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:A7CFKWQOWJIN6LVGYNV4XA3CSS","short_pith_number":"pith:A7CFKWQO","schema_version":"1.0","canonical_sha256":"07c4555a0eb250df2ea6c36bcb83629486264f3de88bbf3eee82a5e47e780dee","source":{"kind":"arxiv","id":"2407.15158","version":1},"attestation_state":"computed","paper":{"title":"HERGen: Elevating Radiology Report Generation with Longitudinal Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuying Wang, Lequan Yu, Shenghui Du","submitted_at":"2024-07-21T13:29:16Z","abstract_excerpt":"Radiology reports provide detailed descriptions of medical imaging integrated with patients' medical histories, while report writing is traditionally labor-intensive, increasing radiologists' workload and the risk of diagnostic errors. Recent efforts in automating this process seek to mitigate these issues by enhancing accuracy and clinical efficiency. Emerging research in automating this process promises to alleviate these challenges by reducing errors and streamlining clinical workflows. However, existing automated approaches are based on a single timestamp and often neglect the critical tem"},"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":"2407.15158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-21T13:29:16Z","cross_cats_sorted":[],"title_canon_sha256":"beed093c57dbda59cd4c068cae9de0ba16f36c8d3a7f504bc70cf096837176d4","abstract_canon_sha256":"f23908d2202de81f61139271310b6e3636c41c544589bbd276c9beddb6a924cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:46:39.656364Z","signature_b64":"mwVQTeA/0U85k3YCiNSNlTeA+GGjMwCm9gCxLe16jaeQYC4s2Tq0CfEoeUWHB2f1lEc7Fn9BOxhAY/NZ6md8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07c4555a0eb250df2ea6c36bcb83629486264f3de88bbf3eee82a5e47e780dee","last_reissued_at":"2026-07-05T08:46:39.655948Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:46:39.655948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HERGen: Elevating Radiology Report Generation with Longitudinal Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuying Wang, Lequan Yu, Shenghui Du","submitted_at":"2024-07-21T13:29:16Z","abstract_excerpt":"Radiology reports provide detailed descriptions of medical imaging integrated with patients' medical histories, while report writing is traditionally labor-intensive, increasing radiologists' workload and the risk of diagnostic errors. Recent efforts in automating this process seek to mitigate these issues by enhancing accuracy and clinical efficiency. Emerging research in automating this process promises to alleviate these challenges by reducing errors and streamlining clinical workflows. However, existing automated approaches are based on a single timestamp and often neglect the critical tem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15158","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/2407.15158/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":"2407.15158","created_at":"2026-07-05T08:46:39.656018+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15158v1","created_at":"2026-07-05T08:46:39.656018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15158","created_at":"2026-07-05T08:46:39.656018+00:00"},{"alias_kind":"pith_short_12","alias_value":"A7CFKWQOWJIN","created_at":"2026-07-05T08:46:39.656018+00:00"},{"alias_kind":"pith_short_16","alias_value":"A7CFKWQOWJIN6LVG","created_at":"2026-07-05T08:46:39.656018+00:00"},{"alias_kind":"pith_short_8","alias_value":"A7CFKWQO","created_at":"2026-07-05T08:46:39.656018+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.06992","citing_title":"MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS","json":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS.json","graph_json":"https://pith.science/api/pith-number/A7CFKWQOWJIN6LVGYNV4XA3CSS/graph.json","events_json":"https://pith.science/api/pith-number/A7CFKWQOWJIN6LVGYNV4XA3CSS/events.json","paper":"https://pith.science/paper/A7CFKWQO"},"agent_actions":{"view_html":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS","download_json":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS.json","view_paper":"https://pith.science/paper/A7CFKWQO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15158&json=true","fetch_graph":"https://pith.science/api/pith-number/A7CFKWQOWJIN6LVGYNV4XA3CSS/graph.json","fetch_events":"https://pith.science/api/pith-number/A7CFKWQOWJIN6LVGYNV4XA3CSS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS/action/storage_attestation","attest_author":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS/action/author_attestation","sign_citation":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS/action/citation_signature","submit_replication":"https://pith.science/pith/A7CFKWQOWJIN6LVGYNV4XA3CSS/action/replication_record"}},"created_at":"2026-07-05T08:46:39.656018+00:00","updated_at":"2026-07-05T08:46:39.656018+00:00"}