{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PXLXWTGGJRVXOYVC2ZHRIF2LMB","short_pith_number":"pith:PXLXWTGG","schema_version":"1.0","canonical_sha256":"7dd77b4cc64c6b7762a2d64f14174b607d88fae561681be0398d584b7d8382de","source":{"kind":"arxiv","id":"1905.06362","version":1},"attestation_state":"computed","paper":{"title":"Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Maier, Bogdan Georgescu, Dorin Comaniciu, Eli Gibson, Florin C. Ghesu, Sasa Grbic, Sebastian Guendel","submitted_at":"2019-05-15T18:09:40Z","abstract_excerpt":"Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities. The large amount of data to be read and reported, with 100+ studies per day for a single radiologist, poses a challenge in maintaining consistently high interpretation accuracy. In this work, we propose a method for the classification of different abnormalities based on CXR scans of the human body. The system is based on a novel multi-task deep learning architecture that in addition to the abnormality classification, supports the segmentation of"},"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":"1905.06362","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-15T18:09:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"018e28ace742aaca3ee6992f3e4aa028beb344ec6dd9d4cf276a81861b9e8e32","abstract_canon_sha256":"21de65e4923978713b0086eee1062e3ba1c9228cb5ec82c4a4d6487dd361ad7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:02.653359Z","signature_b64":"Vbr2JYNEkJ1GLW/Q1ILClcIZwPvpKIgqIvLGvRq+tJd7dw/Y0rcA+vHpUCd81vC2tvygsRUbXHTvLviw0XbKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7dd77b4cc64c6b7762a2d64f14174b607d88fae561681be0398d584b7d8382de","last_reissued_at":"2026-05-17T23:46:02.652599Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:02.652599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Maier, Bogdan Georgescu, Dorin Comaniciu, Eli Gibson, Florin C. Ghesu, Sasa Grbic, Sebastian Guendel","submitted_at":"2019-05-15T18:09:40Z","abstract_excerpt":"Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities. The large amount of data to be read and reported, with 100+ studies per day for a single radiologist, poses a challenge in maintaining consistently high interpretation accuracy. In this work, we propose a method for the classification of different abnormalities based on CXR scans of the human body. The system is based on a novel multi-task deep learning architecture that in addition to the abnormality classification, supports the segmentation of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06362","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":"1905.06362","created_at":"2026-05-17T23:46:02.652723+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.06362v1","created_at":"2026-05-17T23:46:02.652723+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06362","created_at":"2026-05-17T23:46:02.652723+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXLXWTGGJRVX","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXLXWTGGJRVXOYVC","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXLXWTGG","created_at":"2026-05-18T12:33:24.271573+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/PXLXWTGGJRVXOYVC2ZHRIF2LMB","json":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB.json","graph_json":"https://pith.science/api/pith-number/PXLXWTGGJRVXOYVC2ZHRIF2LMB/graph.json","events_json":"https://pith.science/api/pith-number/PXLXWTGGJRVXOYVC2ZHRIF2LMB/events.json","paper":"https://pith.science/paper/PXLXWTGG"},"agent_actions":{"view_html":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB","download_json":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB.json","view_paper":"https://pith.science/paper/PXLXWTGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.06362&json=true","fetch_graph":"https://pith.science/api/pith-number/PXLXWTGGJRVXOYVC2ZHRIF2LMB/graph.json","fetch_events":"https://pith.science/api/pith-number/PXLXWTGGJRVXOYVC2ZHRIF2LMB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB/action/storage_attestation","attest_author":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB/action/author_attestation","sign_citation":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB/action/citation_signature","submit_replication":"https://pith.science/pith/PXLXWTGGJRVXOYVC2ZHRIF2LMB/action/replication_record"}},"created_at":"2026-05-17T23:46:02.652723+00:00","updated_at":"2026-05-17T23:46:02.652723+00:00"}