{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:R6SGVBJ7DXBJINNL5IB4XCSNEK","short_pith_number":"pith:R6SGVBJ7","schema_version":"1.0","canonical_sha256":"8fa46a853f1dc29435abea03cb8a4d229886e2327102cf232f283b64589de751","source":{"kind":"arxiv","id":"2109.05159","version":1},"attestation_state":"computed","paper":{"title":"Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Chuan Sun, Jiarun Liu, Ruirui Li","submitted_at":"2021-09-11T02:09:52Z","abstract_excerpt":"With the development of deep learning, medical image classification has been significantly improved. However, deep learning requires massive data with labels. While labeling the samples by human experts is expensive and time-consuming, collecting labels from crowd-sourcing suffers from the noises which may degenerate the accuracy of classifiers. Therefore, approaches that can effectively handle label noises are highly desired. Unfortunately, recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image. To fill the gap, this paper proposes a noise-tol"},"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":"2109.05159","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-11T02:09:52Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"fa7aced63fd7a61d2321b1b967cee5dd3d300449c82d318693a2115cb952a815","abstract_canon_sha256":"3c4931360e11da2a554bf350aabcde85ebb3c23126cfaeef019b5bd706a506a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:13:35.695803Z","signature_b64":"qTIKo62FLmrNxiJBv3IVv08DmPWV0RleuRQwK5b7iTPTr3MK/CsZ4v9YAtFut+0xgV5PAD7VYXhgqdO7SfhVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fa46a853f1dc29435abea03cb8a4d229886e2327102cf232f283b64589de751","last_reissued_at":"2026-07-05T03:13:35.695308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:13:35.695308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Chuan Sun, Jiarun Liu, Ruirui Li","submitted_at":"2021-09-11T02:09:52Z","abstract_excerpt":"With the development of deep learning, medical image classification has been significantly improved. However, deep learning requires massive data with labels. While labeling the samples by human experts is expensive and time-consuming, collecting labels from crowd-sourcing suffers from the noises which may degenerate the accuracy of classifiers. Therefore, approaches that can effectively handle label noises are highly desired. Unfortunately, recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image. To fill the gap, this paper proposes a noise-tol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.05159","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/2109.05159/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":"2109.05159","created_at":"2026-07-05T03:13:35.695370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.05159v1","created_at":"2026-07-05T03:13:35.695370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.05159","created_at":"2026-07-05T03:13:35.695370+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6SGVBJ7DXBJ","created_at":"2026-07-05T03:13:35.695370+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6SGVBJ7DXBJINNL","created_at":"2026-07-05T03:13:35.695370+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6SGVBJ7","created_at":"2026-07-05T03:13:35.695370+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/R6SGVBJ7DXBJINNL5IB4XCSNEK","json":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK.json","graph_json":"https://pith.science/api/pith-number/R6SGVBJ7DXBJINNL5IB4XCSNEK/graph.json","events_json":"https://pith.science/api/pith-number/R6SGVBJ7DXBJINNL5IB4XCSNEK/events.json","paper":"https://pith.science/paper/R6SGVBJ7"},"agent_actions":{"view_html":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK","download_json":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK.json","view_paper":"https://pith.science/paper/R6SGVBJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.05159&json=true","fetch_graph":"https://pith.science/api/pith-number/R6SGVBJ7DXBJINNL5IB4XCSNEK/graph.json","fetch_events":"https://pith.science/api/pith-number/R6SGVBJ7DXBJINNL5IB4XCSNEK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK/action/storage_attestation","attest_author":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK/action/author_attestation","sign_citation":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK/action/citation_signature","submit_replication":"https://pith.science/pith/R6SGVBJ7DXBJINNL5IB4XCSNEK/action/replication_record"}},"created_at":"2026-07-05T03:13:35.695370+00:00","updated_at":"2026-07-05T03:13:35.695370+00:00"}