{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DM42XYOL6PSMCSJ7RNK7HTASYC","short_pith_number":"pith:DM42XYOL","schema_version":"1.0","canonical_sha256":"1b39abe1cbf3e4c1493f8b55f3cc12c0ab3a2ca5de898c4df2601f976fb6c312","source":{"kind":"arxiv","id":"2111.02400","version":1},"attestation_state":"computed","paper":{"title":"Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","eess.IV","math.OC"],"primary_cat":"cs.LG","authors_text":"Tianbao Yang","submitted_at":"2021-11-01T15:31:32Z","abstract_excerpt":"In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \\underline{\\bf D}eep \\underline{\\bf A}UC \\underline{\\bf M}aximization or {\\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC ma"},"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":"2111.02400","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-01T15:31:32Z","cross_cats_sorted":["cs.AI","cs.CV","eess.IV","math.OC"],"title_canon_sha256":"0e0865fbead7285cbb5bcacebdea48ef926d29510f63759fc1a5a2effc8250a9","abstract_canon_sha256":"db685deda46a00aad7efb78ae3b8c94306b5af14363b5bd90f88a74e46b75980"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:28:51.216358Z","signature_b64":"UC3VuoZ6ch7OzvfmUbZynSdYL4apm7YGrFq1JwNQCxBKqA9HeiEqTpMlSfo7By7y/C+gzkDK8v2uM4gPPUsoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b39abe1cbf3e4c1493f8b55f3cc12c0ab3a2ca5de898c4df2601f976fb6c312","last_reissued_at":"2026-07-05T03:28:51.215961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:28:51.215961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","eess.IV","math.OC"],"primary_cat":"cs.LG","authors_text":"Tianbao Yang","submitted_at":"2021-11-01T15:31:32Z","abstract_excerpt":"In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \\underline{\\bf D}eep \\underline{\\bf A}UC \\underline{\\bf M}aximization or {\\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.02400","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/2111.02400/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":"2111.02400","created_at":"2026-07-05T03:28:51.216014+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.02400v1","created_at":"2026-07-05T03:28:51.216014+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.02400","created_at":"2026-07-05T03:28:51.216014+00:00"},{"alias_kind":"pith_short_12","alias_value":"DM42XYOL6PSM","created_at":"2026-07-05T03:28:51.216014+00:00"},{"alias_kind":"pith_short_16","alias_value":"DM42XYOL6PSMCSJ7","created_at":"2026-07-05T03:28:51.216014+00:00"},{"alias_kind":"pith_short_8","alias_value":"DM42XYOL","created_at":"2026-07-05T03:28:51.216014+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21743","citing_title":"Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC","json":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC.json","graph_json":"https://pith.science/api/pith-number/DM42XYOL6PSMCSJ7RNK7HTASYC/graph.json","events_json":"https://pith.science/api/pith-number/DM42XYOL6PSMCSJ7RNK7HTASYC/events.json","paper":"https://pith.science/paper/DM42XYOL"},"agent_actions":{"view_html":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC","download_json":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC.json","view_paper":"https://pith.science/paper/DM42XYOL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.02400&json=true","fetch_graph":"https://pith.science/api/pith-number/DM42XYOL6PSMCSJ7RNK7HTASYC/graph.json","fetch_events":"https://pith.science/api/pith-number/DM42XYOL6PSMCSJ7RNK7HTASYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC/action/storage_attestation","attest_author":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC/action/author_attestation","sign_citation":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC/action/citation_signature","submit_replication":"https://pith.science/pith/DM42XYOL6PSMCSJ7RNK7HTASYC/action/replication_record"}},"created_at":"2026-07-05T03:28:51.216014+00:00","updated_at":"2026-07-05T03:28:51.216014+00:00"}