{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:M2HOIEPXCOZUWNUPKGYSV3BBVC","short_pith_number":"pith:M2HOIEPX","schema_version":"1.0","canonical_sha256":"668ee411f713b34b368f51b12aec21a897977df6fb449fcea921a62f62ea1155","source":{"kind":"arxiv","id":"2403.06516","version":1},"attestation_state":"computed","paper":{"title":"Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chanyoung Kim, Dayun Ju, Seong Jae Hwang, Woojung Han, Yumin Shim","submitted_at":"2024-03-11T08:43:57Z","abstract_excerpt":"Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports. Nonetheless, to further drive the diffusion models to generate CXRs that faithfully reflect the complexity and diversity of real data, it has become evident that a nontrivial learning approach is needed. In light of this, we propose CXRL, a framework motivated by the potential of reinforcement learning (RL). Specifically, we integrate a policy gradient RL approach with well-designed"},"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":"2403.06516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-11T08:43:57Z","cross_cats_sorted":[],"title_canon_sha256":"e3e1f7300a8a422240c22a8a46d910aa61b40c60e2b8ad0ee69090412ed96b27","abstract_canon_sha256":"cbe2b8f3c04e1562ef5a8693539bff7dab2f48ded706f40f42e80475c61f59bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:39.288515Z","signature_b64":"jFyUFQ6S4YSYgQ3o1c18gWT/PqD8B6hzK+O8mxMk0BOt0jy1VUB3GwimVAoPWG1OOCqAhT4z7nFjiCqcKaOXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"668ee411f713b34b368f51b12aec21a897977df6fb449fcea921a62f62ea1155","last_reissued_at":"2026-07-05T07:54:39.288086Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:39.288086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chanyoung Kim, Dayun Ju, Seong Jae Hwang, Woojung Han, Yumin Shim","submitted_at":"2024-03-11T08:43:57Z","abstract_excerpt":"Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports. Nonetheless, to further drive the diffusion models to generate CXRs that faithfully reflect the complexity and diversity of real data, it has become evident that a nontrivial learning approach is needed. In light of this, we propose CXRL, a framework motivated by the potential of reinforcement learning (RL). Specifically, we integrate a policy gradient RL approach with well-designed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06516","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/2403.06516/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":"2403.06516","created_at":"2026-07-05T07:54:39.288142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06516v1","created_at":"2026-07-05T07:54:39.288142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06516","created_at":"2026-07-05T07:54:39.288142+00:00"},{"alias_kind":"pith_short_12","alias_value":"M2HOIEPXCOZU","created_at":"2026-07-05T07:54:39.288142+00:00"},{"alias_kind":"pith_short_16","alias_value":"M2HOIEPXCOZUWNUP","created_at":"2026-07-05T07:54:39.288142+00:00"},{"alias_kind":"pith_short_8","alias_value":"M2HOIEPX","created_at":"2026-07-05T07:54:39.288142+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05573","citing_title":"Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC","json":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC.json","graph_json":"https://pith.science/api/pith-number/M2HOIEPXCOZUWNUPKGYSV3BBVC/graph.json","events_json":"https://pith.science/api/pith-number/M2HOIEPXCOZUWNUPKGYSV3BBVC/events.json","paper":"https://pith.science/paper/M2HOIEPX"},"agent_actions":{"view_html":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC","download_json":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC.json","view_paper":"https://pith.science/paper/M2HOIEPX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06516&json=true","fetch_graph":"https://pith.science/api/pith-number/M2HOIEPXCOZUWNUPKGYSV3BBVC/graph.json","fetch_events":"https://pith.science/api/pith-number/M2HOIEPXCOZUWNUPKGYSV3BBVC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC/action/storage_attestation","attest_author":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC/action/author_attestation","sign_citation":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC/action/citation_signature","submit_replication":"https://pith.science/pith/M2HOIEPXCOZUWNUPKGYSV3BBVC/action/replication_record"}},"created_at":"2026-07-05T07:54:39.288142+00:00","updated_at":"2026-07-05T07:54:39.288142+00:00"}