{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:63PPEHEX624TUMH3Y6FCVD4TNN","short_pith_number":"pith:63PPEHEX","schema_version":"1.0","canonical_sha256":"f6def21c97f6b93a30fbc78a2a8f936b51fbf42a532865c6006d484ae45a9ac4","source":{"kind":"arxiv","id":"2405.17459","version":1},"attestation_state":"computed","paper":{"title":"Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Fei Lin, Lu Dai, Sheng Chai, Weijie He, Xinghui Fei, Ziyan Yao","submitted_at":"2024-05-23T02:22:10Z","abstract_excerpt":"In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features "},"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":"2405.17459","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-23T02:22:10Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"870c1c557e4a78ab2c31ae2e2d7ef148b4a597682b0acf31cfba479d25f44167","abstract_canon_sha256":"1438b3540c34169c876aa350b84121d9c574a4fe9267daf027168c8318e370c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:07.579613Z","signature_b64":"jDlP5dNitAsQdlR13ApEiB9mrg3hRep6X2SH9k7NbCQLfRbOXgxZvNaDaJsOIvBdf7hDaBcliushKX9HbcNfAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6def21c97f6b93a30fbc78a2a8f936b51fbf42a532865c6006d484ae45a9ac4","last_reissued_at":"2026-07-05T08:24:07.579040Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:07.579040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Fei Lin, Lu Dai, Sheng Chai, Weijie He, Xinghui Fei, Ziyan Yao","submitted_at":"2024-05-23T02:22:10Z","abstract_excerpt":"In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17459","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/2405.17459/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":"2405.17459","created_at":"2026-07-05T08:24:07.579106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.17459v1","created_at":"2026-07-05T08:24:07.579106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17459","created_at":"2026-07-05T08:24:07.579106+00:00"},{"alias_kind":"pith_short_12","alias_value":"63PPEHEX624T","created_at":"2026-07-05T08:24:07.579106+00:00"},{"alias_kind":"pith_short_16","alias_value":"63PPEHEX624TUMH3","created_at":"2026-07-05T08:24:07.579106+00:00"},{"alias_kind":"pith_short_8","alias_value":"63PPEHEX","created_at":"2026-07-05T08:24:07.579106+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/63PPEHEX624TUMH3Y6FCVD4TNN","json":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN.json","graph_json":"https://pith.science/api/pith-number/63PPEHEX624TUMH3Y6FCVD4TNN/graph.json","events_json":"https://pith.science/api/pith-number/63PPEHEX624TUMH3Y6FCVD4TNN/events.json","paper":"https://pith.science/paper/63PPEHEX"},"agent_actions":{"view_html":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN","download_json":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN.json","view_paper":"https://pith.science/paper/63PPEHEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.17459&json=true","fetch_graph":"https://pith.science/api/pith-number/63PPEHEX624TUMH3Y6FCVD4TNN/graph.json","fetch_events":"https://pith.science/api/pith-number/63PPEHEX624TUMH3Y6FCVD4TNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN/action/storage_attestation","attest_author":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN/action/author_attestation","sign_citation":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN/action/citation_signature","submit_replication":"https://pith.science/pith/63PPEHEX624TUMH3Y6FCVD4TNN/action/replication_record"}},"created_at":"2026-07-05T08:24:07.579106+00:00","updated_at":"2026-07-05T08:24:07.579106+00:00"}