{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3H5YESGAOKWFZ4Z622YHUWPEUP","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":"567139cba829f1ed34412f3a25a308f9f8dc50923cd731416c544bd865a70bbe","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-21T04:47:42Z","title_canon_sha256":"4fad4b1829ee1f40ef4f15db6b92b2202f8a4ea6cf9aca890f93ccf951bf6d1d"},"schema_version":"1.0","source":{"id":"2109.09960","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09960","created_at":"2026-07-05T04:37:02Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09960v4","created_at":"2026-07-05T04:37:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09960","created_at":"2026-07-05T04:37:02Z"},{"alias_kind":"pith_short_12","alias_value":"3H5YESGAOKWF","created_at":"2026-07-05T04:37:02Z"},{"alias_kind":"pith_short_16","alias_value":"3H5YESGAOKWFZ4Z6","created_at":"2026-07-05T04:37:02Z"},{"alias_kind":"pith_short_8","alias_value":"3H5YESGA","created_at":"2026-07-05T04:37:02Z"}],"graph_snapshots":[{"event_id":"sha256:a9ccbcafeee6fe6c0a2f35b40afdb5c0a4f3c21a54a4ae19af49d92212e51910","target":"graph","created_at":"2026-07-05T04:37:02Z","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/2109.09960/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new","authors_text":"Donghao Zhang, Jianfei Cai, Lei Zhang, Minfeng Xu, Yicheng Wu, Yong Xia, Zongyuan Ge","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-21T04:47:42Z","title":"Mutual Consistency Learning for Semi-supervised Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09960","kind":"arxiv","version":4},"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:f402501ad4154be60d7e06672ccd039a53cc1381d4eba1a36406640fac85d84d","target":"record","created_at":"2026-07-05T04:37:02Z","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":"567139cba829f1ed34412f3a25a308f9f8dc50923cd731416c544bd865a70bbe","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-21T04:47:42Z","title_canon_sha256":"4fad4b1829ee1f40ef4f15db6b92b2202f8a4ea6cf9aca890f93ccf951bf6d1d"},"schema_version":"1.0","source":{"id":"2109.09960","kind":"arxiv","version":4}},"canonical_sha256":"d9fb8248c072ac5cf33ed6b07a59e4a3ffbec1edea577cde7e3ee2f04779f3a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9fb8248c072ac5cf33ed6b07a59e4a3ffbec1edea577cde7e3ee2f04779f3a7","first_computed_at":"2026-07-05T04:37:02.386854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:37:02.386854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NhnbltqRZ+Qyed3hzbayXxIsy1EG3OTOHtsUOWJCqx+1um/Ae3shroh9djQ/KIpphd/dROqzhx/hOulA5IduAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:37:02.387253Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.09960","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f402501ad4154be60d7e06672ccd039a53cc1381d4eba1a36406640fac85d84d","sha256:a9ccbcafeee6fe6c0a2f35b40afdb5c0a4f3c21a54a4ae19af49d92212e51910"],"state_sha256":"28efe820166d9942a14ddbd3889c18a718ab0f7214179f864b9a44bdc1f2ec7d"}