{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:EPA2E2O3HOYO4VKC4F3Q6HNOTW","short_pith_number":"pith:EPA2E2O3","schema_version":"1.0","canonical_sha256":"23c1a269db3bb0ee5542e1770f1dae9d906ea4627163759687b44f23ea148268","source":{"kind":"arxiv","id":"2111.11629","version":2},"attestation_state":"computed","paper":{"title":"Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chiu-Wing Sham, Chong Fu, Haoyu Xie, Jialei Chen, Xingwei Wang, Xu Zheng","submitted_at":"2021-11-23T03:26:24Z","abstract_excerpt":"Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we p"},"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":"2111.11629","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-23T03:26:24Z","cross_cats_sorted":[],"title_canon_sha256":"e1f7bf97c187db58a6dc91bf5ca0f7d82c15e49386d33bd1204201074115342f","abstract_canon_sha256":"a43e1cb51c1645609d3e036e230a08bec96c0f46b062cb36ac74811e4fe369c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:36:58.695905Z","signature_b64":"TURebqxyT4mTGrG97ONH3Vy4ExtF9Vf/0r6PETfA3k60v8G4TznSv/nzktVp+bA0dAGmDqo5RT09ahkBhWGTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23c1a269db3bb0ee5542e1770f1dae9d906ea4627163759687b44f23ea148268","last_reissued_at":"2026-07-05T03:36:58.695403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:36:58.695403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chiu-Wing Sham, Chong Fu, Haoyu Xie, Jialei Chen, Xingwei Wang, Xu Zheng","submitted_at":"2021-11-23T03:26:24Z","abstract_excerpt":"Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.11629","kind":"arxiv","version":2},"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/2111.11629/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":"2111.11629","created_at":"2026-07-05T03:36:58.695467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.11629v2","created_at":"2026-07-05T03:36:58.695467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11629","created_at":"2026-07-05T03:36:58.695467+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPA2E2O3HOYO","created_at":"2026-07-05T03:36:58.695467+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPA2E2O3HOYO4VKC","created_at":"2026-07-05T03:36:58.695467+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPA2E2O3","created_at":"2026-07-05T03:36:58.695467+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/EPA2E2O3HOYO4VKC4F3Q6HNOTW","json":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW.json","graph_json":"https://pith.science/api/pith-number/EPA2E2O3HOYO4VKC4F3Q6HNOTW/graph.json","events_json":"https://pith.science/api/pith-number/EPA2E2O3HOYO4VKC4F3Q6HNOTW/events.json","paper":"https://pith.science/paper/EPA2E2O3"},"agent_actions":{"view_html":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW","download_json":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW.json","view_paper":"https://pith.science/paper/EPA2E2O3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.11629&json=true","fetch_graph":"https://pith.science/api/pith-number/EPA2E2O3HOYO4VKC4F3Q6HNOTW/graph.json","fetch_events":"https://pith.science/api/pith-number/EPA2E2O3HOYO4VKC4F3Q6HNOTW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW/action/storage_attestation","attest_author":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW/action/author_attestation","sign_citation":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW/action/citation_signature","submit_replication":"https://pith.science/pith/EPA2E2O3HOYO4VKC4F3Q6HNOTW/action/replication_record"}},"created_at":"2026-07-05T03:36:58.695467+00:00","updated_at":"2026-07-05T03:36:58.695467+00:00"}