{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:47D7RIYBKFWLBYJZM2CDAIQD2G","short_pith_number":"pith:47D7RIYB","schema_version":"1.0","canonical_sha256":"e7c7f8a301516cb0e1396684302203d19282d4c60d53c39f83eded5e54e738fd","source":{"kind":"arxiv","id":"1801.09927","version":1},"attestation_state":"computed","paper":{"title":"Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liang Zheng, Qingji Guan, Yaping Huang, Yi Yang, Zhedong Zheng, Zhun Zhong","submitted_at":"2018-01-30T10:55:23Z","abstract_excerpt":"This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided co"},"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":"1801.09927","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-30T10:55:23Z","cross_cats_sorted":[],"title_canon_sha256":"055dc0e8f06f356491b0f57e3a3d5c545c060db835cd2c8ed5e93147ebc4e03d","abstract_canon_sha256":"cf07131a425c85d3df547a400888caddffc1077656d38dea48d5e417d238f1b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:46.368694Z","signature_b64":"6H4H0MZCO56G7BULCmCcbnaUYkRJExrqSDTZNK001MxvVHqBifF9DDK5WTN0aRS0W6bpj2QY8EQ73vycsvVJBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7c7f8a301516cb0e1396684302203d19282d4c60d53c39f83eded5e54e738fd","last_reissued_at":"2026-05-18T00:24:46.368118Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:46.368118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liang Zheng, Qingji Guan, Yaping Huang, Yi Yang, Zhedong Zheng, Zhun Zhong","submitted_at":"2018-01-30T10:55:23Z","abstract_excerpt":"This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.09927","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":"1801.09927","created_at":"2026-05-18T00:24:46.368204+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.09927v1","created_at":"2026-05-18T00:24:46.368204+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.09927","created_at":"2026-05-18T00:24:46.368204+00:00"},{"alias_kind":"pith_short_12","alias_value":"47D7RIYBKFWL","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"47D7RIYBKFWLBYJZ","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"47D7RIYB","created_at":"2026-05-18T12:32:05.422762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.13842","citing_title":"From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies","ref_index":284,"is_internal_anchor":true},{"citing_arxiv_id":"2605.02292","citing_title":"Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G","json":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G.json","graph_json":"https://pith.science/api/pith-number/47D7RIYBKFWLBYJZM2CDAIQD2G/graph.json","events_json":"https://pith.science/api/pith-number/47D7RIYBKFWLBYJZM2CDAIQD2G/events.json","paper":"https://pith.science/paper/47D7RIYB"},"agent_actions":{"view_html":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G","download_json":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G.json","view_paper":"https://pith.science/paper/47D7RIYB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.09927&json=true","fetch_graph":"https://pith.science/api/pith-number/47D7RIYBKFWLBYJZM2CDAIQD2G/graph.json","fetch_events":"https://pith.science/api/pith-number/47D7RIYBKFWLBYJZM2CDAIQD2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G/action/storage_attestation","attest_author":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G/action/author_attestation","sign_citation":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G/action/citation_signature","submit_replication":"https://pith.science/pith/47D7RIYBKFWLBYJZM2CDAIQD2G/action/replication_record"}},"created_at":"2026-05-18T00:24:46.368204+00:00","updated_at":"2026-05-18T00:24:46.368204+00:00"}