{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SHJOUOU7M6QEYYR4C7IJNESRPP","short_pith_number":"pith:SHJOUOU7","schema_version":"1.0","canonical_sha256":"91d2ea3a9f67a04c623c17d09692517bcd71787dc5141a0a638d26eb824b2d80","source":{"kind":"arxiv","id":"2505.06903","version":1},"attestation_state":"computed","paper":{"title":"CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianxin Wang, Junwen Duan, Xinyu Li, Yuanzhuo Wang","submitted_at":"2025-05-11T08:51:38Z","abstract_excerpt":"Temporal medical image analysis is essential for clinical decision-making, yet existing methods either align images and text at a coarse level - causing potential semantic mismatches - or depend solely on visual information, lacking medical semantic integration. We present CheXLearner, the first end-to-end framework that unifies anatomical region detection, Riemannian manifold-based structure alignment, and fine-grained regional semantic guidance. Our proposed Med-Manifold Alignment Module (Med-MAM) leverages hyperbolic geometry to robustly align anatomical structures and capture pathologicall"},"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":"2505.06903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-11T08:51:38Z","cross_cats_sorted":[],"title_canon_sha256":"428a67677c721a2381073bad358b274bd2b550dab9a3dfd86486f2bd790c2493","abstract_canon_sha256":"7313a7138d7b52d93a8518420909f24d5379ca465e878285f458dc55774e288d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:38.972050Z","signature_b64":"Y+iiO723EWcdVwACHbE34al8bGuOOsEW42nTC8apSg61Qex0acVJtwHi/5tjriOsptnTm6Er66GCSxOmZpSWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91d2ea3a9f67a04c623c17d09692517bcd71787dc5141a0a638d26eb824b2d80","last_reissued_at":"2026-07-05T11:01:38.971551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:38.971551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianxin Wang, Junwen Duan, Xinyu Li, Yuanzhuo Wang","submitted_at":"2025-05-11T08:51:38Z","abstract_excerpt":"Temporal medical image analysis is essential for clinical decision-making, yet existing methods either align images and text at a coarse level - causing potential semantic mismatches - or depend solely on visual information, lacking medical semantic integration. We present CheXLearner, the first end-to-end framework that unifies anatomical region detection, Riemannian manifold-based structure alignment, and fine-grained regional semantic guidance. Our proposed Med-Manifold Alignment Module (Med-MAM) leverages hyperbolic geometry to robustly align anatomical structures and capture pathologicall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06903","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/2505.06903/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":"2505.06903","created_at":"2026-07-05T11:01:38.971610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.06903v1","created_at":"2026-07-05T11:01:38.971610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06903","created_at":"2026-07-05T11:01:38.971610+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHJOUOU7M6QE","created_at":"2026-07-05T11:01:38.971610+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHJOUOU7M6QEYYR4","created_at":"2026-07-05T11:01:38.971610+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHJOUOU7","created_at":"2026-07-05T11:01:38.971610+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/SHJOUOU7M6QEYYR4C7IJNESRPP","json":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP.json","graph_json":"https://pith.science/api/pith-number/SHJOUOU7M6QEYYR4C7IJNESRPP/graph.json","events_json":"https://pith.science/api/pith-number/SHJOUOU7M6QEYYR4C7IJNESRPP/events.json","paper":"https://pith.science/paper/SHJOUOU7"},"agent_actions":{"view_html":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP","download_json":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP.json","view_paper":"https://pith.science/paper/SHJOUOU7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.06903&json=true","fetch_graph":"https://pith.science/api/pith-number/SHJOUOU7M6QEYYR4C7IJNESRPP/graph.json","fetch_events":"https://pith.science/api/pith-number/SHJOUOU7M6QEYYR4C7IJNESRPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP/action/storage_attestation","attest_author":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP/action/author_attestation","sign_citation":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP/action/citation_signature","submit_replication":"https://pith.science/pith/SHJOUOU7M6QEYYR4C7IJNESRPP/action/replication_record"}},"created_at":"2026-07-05T11:01:38.971610+00:00","updated_at":"2026-07-05T11:01:38.971610+00:00"}