{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:TGBET6RALZDXNBO5RCUYMAKEU6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d2d332e93d4ab99e5183309307841d84f3da892bc744c5fb9d2947c29f16f39c","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-14T21:16:52Z","title_canon_sha256":"79027f7c56a99cceac3beeaa2e703e59f853cb6751112f2d86735231a192278f"},"schema_version":"1.0","source":{"id":"2112.08363","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08363","created_at":"2026-07-05T03:41:28Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08363v1","created_at":"2026-07-05T03:41:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08363","created_at":"2026-07-05T03:41:28Z"},{"alias_kind":"pith_short_12","alias_value":"TGBET6RALZDX","created_at":"2026-07-05T03:41:28Z"},{"alias_kind":"pith_short_16","alias_value":"TGBET6RALZDXNBO5","created_at":"2026-07-05T03:41:28Z"},{"alias_kind":"pith_short_8","alias_value":"TGBET6RA","created_at":"2026-07-05T03:41:28Z"}],"graph_snapshots":[{"event_id":"sha256:56fbf5f3ec7afbf88584851910cd8bcdbdb0d81fd4c4b69b135ecf9914ebde53","target":"graph","created_at":"2026-07-05T03:41:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2112.08363/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a ne","authors_text":"Alexander Wong, Ashkan Ebadi, Pengcheng Xi, Siyuan He, Stephane Tremblay","cross_cats":["cs.CV","eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-14T21:16:52Z","title":"Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08363","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:73f0304679f4f5b71b959bc8e7394f3c2b82824bdd0cc3c9dadc4437b9a78f1b","target":"record","created_at":"2026-07-05T03:41:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d2d332e93d4ab99e5183309307841d84f3da892bc744c5fb9d2947c29f16f39c","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-14T21:16:52Z","title_canon_sha256":"79027f7c56a99cceac3beeaa2e703e59f853cb6751112f2d86735231a192278f"},"schema_version":"1.0","source":{"id":"2112.08363","kind":"arxiv","version":1}},"canonical_sha256":"998249fa205e477685dd88a9860144a79b709244cb2b3b2542ba00db1b551e53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"998249fa205e477685dd88a9860144a79b709244cb2b3b2542ba00db1b551e53","first_computed_at":"2026-07-05T03:41:28.870171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:41:28.870171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BkFBlzoWzoWcUGBS6mrT0J0KUEVom3VzTdpbbeT1d9IrAyKXl03Dczr7tONZ5pdqdyZ1bN7ZOI2UqxV1uiFFCA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:41:28.870691Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.08363","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:73f0304679f4f5b71b959bc8e7394f3c2b82824bdd0cc3c9dadc4437b9a78f1b","sha256:56fbf5f3ec7afbf88584851910cd8bcdbdb0d81fd4c4b69b135ecf9914ebde53"],"state_sha256":"695eba76e56e2841444cc47998df0ddf7af238e797a026292caa25e7e5268322"}