{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XT46BYP4SEUMSNCZB5E6GTUUAE","short_pith_number":"pith:XT46BYP4","schema_version":"1.0","canonical_sha256":"bcf9e0e1fc9128c934590f49e34e940106cc8bd1757013131bb54c9f4c59a699","source":{"kind":"arxiv","id":"1807.00958","version":1},"attestation_state":"computed","paper":{"title":"Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam P. Harrison, Jinzheng Cai, Le Lu, Lin Yang, Pingjun Chen, Xiaoshuang Shi","submitted_at":"2018-07-03T02:56:38Z","abstract_excerpt":"Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pa"},"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":"1807.00958","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T02:56:38Z","cross_cats_sorted":[],"title_canon_sha256":"8c2f04cc96351f9be698fd16ed03591bb1f3262d0e3c021133f0f06fe4490003","abstract_canon_sha256":"878e6a6c088858c2a7bddb5fb9d32e79fffaf12fe3867112d502ec9f07a36330"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:45.880245Z","signature_b64":"sv1RW1yXynk2Uj/QVjPGXumWSkLAOm4goeUq69+tBIRVkjf//gbUyxYBHFr8FC8lkJiKZgwVo/O93NuPwRUpAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bcf9e0e1fc9128c934590f49e34e940106cc8bd1757013131bb54c9f4c59a699","last_reissued_at":"2026-05-18T00:11:45.879529Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:45.879529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam P. Harrison, Jinzheng Cai, Le Lu, Lin Yang, Pingjun Chen, Xiaoshuang Shi","submitted_at":"2018-07-03T02:56:38Z","abstract_excerpt":"Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00958","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":"1807.00958","created_at":"2026-05-18T00:11:45.879646+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00958v1","created_at":"2026-05-18T00:11:45.879646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00958","created_at":"2026-05-18T00:11:45.879646+00:00"},{"alias_kind":"pith_short_12","alias_value":"XT46BYP4SEUM","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XT46BYP4SEUMSNCZ","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XT46BYP4","created_at":"2026-05-18T12:33:01.666342+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/XT46BYP4SEUMSNCZB5E6GTUUAE","json":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE.json","graph_json":"https://pith.science/api/pith-number/XT46BYP4SEUMSNCZB5E6GTUUAE/graph.json","events_json":"https://pith.science/api/pith-number/XT46BYP4SEUMSNCZB5E6GTUUAE/events.json","paper":"https://pith.science/paper/XT46BYP4"},"agent_actions":{"view_html":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE","download_json":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE.json","view_paper":"https://pith.science/paper/XT46BYP4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00958&json=true","fetch_graph":"https://pith.science/api/pith-number/XT46BYP4SEUMSNCZB5E6GTUUAE/graph.json","fetch_events":"https://pith.science/api/pith-number/XT46BYP4SEUMSNCZB5E6GTUUAE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE/action/storage_attestation","attest_author":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE/action/author_attestation","sign_citation":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE/action/citation_signature","submit_replication":"https://pith.science/pith/XT46BYP4SEUMSNCZB5E6GTUUAE/action/replication_record"}},"created_at":"2026-05-18T00:11:45.879646+00:00","updated_at":"2026-05-18T00:11:45.879646+00:00"}