{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YIZVXJGKKKAYSQDZGMWH7ZDI7M","short_pith_number":"pith:YIZVXJGK","schema_version":"1.0","canonical_sha256":"c2335ba4ca5281894079332c7fe468fb09adf7da7f47c9fbadd29114ba5c1b2d","source":{"kind":"arxiv","id":"2509.04819","version":2},"attestation_state":"computed","paper":{"title":"AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hoifung Poon, Jiang Bian, Jingjing Fu, Mu Wei, Naiteek Sangani, Nan Liu, Paul Vozila, Shuhan Ding, Yu Gu","submitted_at":"2025-09-05T05:40:55Z","abstract_excerpt":"Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and controllable synthesis remains difficult due to limited high-quality annotations and domain shifts across datasets. Existing methods, often designed for natural images or well-defined tumors, struggle to generalize to chest radiographs, where disease patterns are morphologically diverse and tightly intertwined with anatomical structures. To address these challenges, we propose AURAD, a controllable radiology synthesis fr"},"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":"2509.04819","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-09-05T05:40:55Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b1ae1b656f7b2ba714583ac7e3223fa9436af64359eba684031d183acf5668ea","abstract_canon_sha256":"1312a2a8c2639531f822f3b4deecf8a7005b6596ca71b144ac25a0db8b2fe0c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:49.851579Z","signature_b64":"ZpCpTqVc4sDSw/I8cno/zG/BKWQS0sW+5TqhlYAlcntabrYtMnXBaXN5ihZl362rFltKNJU2jkY4tvlEmGorAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2335ba4ca5281894079332c7fe468fb09adf7da7f47c9fbadd29114ba5c1b2d","last_reissued_at":"2026-07-05T12:06:49.851074Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:49.851074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hoifung Poon, Jiang Bian, Jingjing Fu, Mu Wei, Naiteek Sangani, Nan Liu, Paul Vozila, Shuhan Ding, Yu Gu","submitted_at":"2025-09-05T05:40:55Z","abstract_excerpt":"Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and controllable synthesis remains difficult due to limited high-quality annotations and domain shifts across datasets. Existing methods, often designed for natural images or well-defined tumors, struggle to generalize to chest radiographs, where disease patterns are morphologically diverse and tightly intertwined with anatomical structures. To address these challenges, we propose AURAD, a controllable radiology synthesis fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04819","kind":"arxiv","version":2},"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/2509.04819/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":"2509.04819","created_at":"2026-07-05T12:06:49.851134+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.04819v2","created_at":"2026-07-05T12:06:49.851134+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04819","created_at":"2026-07-05T12:06:49.851134+00:00"},{"alias_kind":"pith_short_12","alias_value":"YIZVXJGKKKAY","created_at":"2026-07-05T12:06:49.851134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YIZVXJGKKKAYSQDZ","created_at":"2026-07-05T12:06:49.851134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YIZVXJGK","created_at":"2026-07-05T12:06:49.851134+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/YIZVXJGKKKAYSQDZGMWH7ZDI7M","json":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M.json","graph_json":"https://pith.science/api/pith-number/YIZVXJGKKKAYSQDZGMWH7ZDI7M/graph.json","events_json":"https://pith.science/api/pith-number/YIZVXJGKKKAYSQDZGMWH7ZDI7M/events.json","paper":"https://pith.science/paper/YIZVXJGK"},"agent_actions":{"view_html":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M","download_json":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M.json","view_paper":"https://pith.science/paper/YIZVXJGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.04819&json=true","fetch_graph":"https://pith.science/api/pith-number/YIZVXJGKKKAYSQDZGMWH7ZDI7M/graph.json","fetch_events":"https://pith.science/api/pith-number/YIZVXJGKKKAYSQDZGMWH7ZDI7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M/action/storage_attestation","attest_author":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M/action/author_attestation","sign_citation":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M/action/citation_signature","submit_replication":"https://pith.science/pith/YIZVXJGKKKAYSQDZGMWH7ZDI7M/action/replication_record"}},"created_at":"2026-07-05T12:06:49.851134+00:00","updated_at":"2026-07-05T12:06:49.851134+00:00"}