{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZTIIAHKQ3MC26YZKSWKVXUE7II","short_pith_number":"pith:ZTIIAHKQ","schema_version":"1.0","canonical_sha256":"ccd0801d50db05af632a95955bd09f423f0ddeccec6cf41cb84a32ac862746c6","source":{"kind":"arxiv","id":"1708.03070","version":1},"attestation_state":"computed","paper":{"title":"TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lin Yang, Manish Sapkota, Pingjun Chen, Zizhao Zhang","submitted_at":"2017-08-10T04:12:00Z","abstract_excerpt":"In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make"},"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":"1708.03070","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-10T04:12:00Z","cross_cats_sorted":[],"title_canon_sha256":"bf5fab3c54cb6293ae782239d906d20ca4fc66fc92dd130d2dab90c7e98fcfb6","abstract_canon_sha256":"b27fb7077161694fac528515c116af5d7fd5b6a819f00f4087660a252abcbdb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:14.869340Z","signature_b64":"Vw/WEK3N//JPUdPWdL5G3dhIOJhKrZCVDLDu3JIOIuULLzFA7CKRVmZ1UZf07kIIHzkhrakVlCTQeHD407LfDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccd0801d50db05af632a95955bd09f423f0ddeccec6cf41cb84a32ac862746c6","last_reissued_at":"2026-05-18T00:38:14.868661Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:14.868661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lin Yang, Manish Sapkota, Pingjun Chen, Zizhao Zhang","submitted_at":"2017-08-10T04:12:00Z","abstract_excerpt":"In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.03070","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":""},"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":"1708.03070","created_at":"2026-05-18T00:38:14.868789+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.03070v1","created_at":"2026-05-18T00:38:14.868789+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.03070","created_at":"2026-05-18T00:38:14.868789+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZTIIAHKQ3MC2","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZTIIAHKQ3MC26YZK","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZTIIAHKQ","created_at":"2026-05-18T12:31:59.375834+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/ZTIIAHKQ3MC26YZKSWKVXUE7II","json":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II.json","graph_json":"https://pith.science/api/pith-number/ZTIIAHKQ3MC26YZKSWKVXUE7II/graph.json","events_json":"https://pith.science/api/pith-number/ZTIIAHKQ3MC26YZKSWKVXUE7II/events.json","paper":"https://pith.science/paper/ZTIIAHKQ"},"agent_actions":{"view_html":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II","download_json":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II.json","view_paper":"https://pith.science/paper/ZTIIAHKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.03070&json=true","fetch_graph":"https://pith.science/api/pith-number/ZTIIAHKQ3MC26YZKSWKVXUE7II/graph.json","fetch_events":"https://pith.science/api/pith-number/ZTIIAHKQ3MC26YZKSWKVXUE7II/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II/action/storage_attestation","attest_author":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II/action/author_attestation","sign_citation":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II/action/citation_signature","submit_replication":"https://pith.science/pith/ZTIIAHKQ3MC26YZKSWKVXUE7II/action/replication_record"}},"created_at":"2026-05-18T00:38:14.868789+00:00","updated_at":"2026-05-18T00:38:14.868789+00:00"}