{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:A5L5MIFVQDGT42R2EV4BCPO27Q","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":"757e781de0f9c0ef4957a80c3944289a209f49d75d669fee0b79db6b7df4dc9d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-17T05:29:50Z","title_canon_sha256":"f48d630f864b1c1bd316c693d24caa199a8fc7b4a885d798798d3cf89c3e6d1b"},"schema_version":"1.0","source":{"id":"2502.11456","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11456","created_at":"2026-07-05T10:15:29Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11456v1","created_at":"2026-07-05T10:15:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11456","created_at":"2026-07-05T10:15:29Z"},{"alias_kind":"pith_short_12","alias_value":"A5L5MIFVQDGT","created_at":"2026-07-05T10:15:29Z"},{"alias_kind":"pith_short_16","alias_value":"A5L5MIFVQDGT42R2","created_at":"2026-07-05T10:15:29Z"},{"alias_kind":"pith_short_8","alias_value":"A5L5MIFV","created_at":"2026-07-05T10:15:29Z"}],"graph_snapshots":[{"event_id":"sha256:0d50f6c836b7c63e73c2051175ae449ad3a25e2dd8ba6f567e3939c4ea7e0993","target":"graph","created_at":"2026-07-05T10:15:29Z","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/2502.11456/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of this strategy depends on the accuracy of the pseudo-labels generated by the model for this consistency learning strategy. In this paper, we introduce a new methodology to produce high-quality pseudo-labe","authors_text":"Gustavo Carneiro, Kechen Song, Shuai Ma, Yanyan Wang, Yunhui Yan, Yuyuan Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-17T05:29:50Z","title":"Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11456","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":"sha256:0a8c2212fea2bf19e9ab18bb3b556dabdbb683923b2f393fdf5dd4dc698e78dd","target":"record","created_at":"2026-07-05T10:15:29Z","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":"757e781de0f9c0ef4957a80c3944289a209f49d75d669fee0b79db6b7df4dc9d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-17T05:29:50Z","title_canon_sha256":"f48d630f864b1c1bd316c693d24caa199a8fc7b4a885d798798d3cf89c3e6d1b"},"schema_version":"1.0","source":{"id":"2502.11456","kind":"arxiv","version":1}},"canonical_sha256":"0757d620b580cd3e6a3a2578113ddafc384efb565b8b026c691c12668b26a3e0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0757d620b580cd3e6a3a2578113ddafc384efb565b8b026c691c12668b26a3e0","first_computed_at":"2026-07-05T10:15:29.000218Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:15:29.000218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CvmAmSqz8wjglH1stT1VNHYMPtasUW9fXZNAwb7n1vJOpMqFm5iDte1KhFFD6mMBUy1E5n3+jPwpC9jZ62NpDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:15:29.000729Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11456","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a8c2212fea2bf19e9ab18bb3b556dabdbb683923b2f393fdf5dd4dc698e78dd","sha256:0d50f6c836b7c63e73c2051175ae449ad3a25e2dd8ba6f567e3939c4ea7e0993"],"state_sha256":"a410f492a271e4c2cedb5f5eb79906e9ea48cbf446c76d833ba94655e09baac4"}