{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DDP5U4TZFCK6PGV2PG5XORYNQZ","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":"e3d3c9940d14dc396613d1cbe66128bcda9f77fe1acb322f95853baf9eba6453","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-03T15:19:59Z","title_canon_sha256":"5a240e9848004b279637c42e54974bda6341b639813032c7a0545dd9f483d014"},"schema_version":"1.0","source":{"id":"2211.01886","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.01886","created_at":"2026-07-05T06:04:52Z"},{"alias_kind":"arxiv_version","alias_value":"2211.01886v1","created_at":"2026-07-05T06:04:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.01886","created_at":"2026-07-05T06:04:52Z"},{"alias_kind":"pith_short_12","alias_value":"DDP5U4TZFCK6","created_at":"2026-07-05T06:04:52Z"},{"alias_kind":"pith_short_16","alias_value":"DDP5U4TZFCK6PGV2","created_at":"2026-07-05T06:04:52Z"},{"alias_kind":"pith_short_8","alias_value":"DDP5U4TZ","created_at":"2026-07-05T06:04:52Z"}],"graph_snapshots":[{"event_id":"sha256:deffcda9a9d5dec2f3f3836dfe10f9a30d42429fb8ccd38911d648d568f7033b","target":"graph","created_at":"2026-07-05T06:04:52Z","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/2211.01886/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. However, training these models requires access to large amounts of manually labelled data which is often difficult to obtain in real medical applications. In such settings, semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Recently,","authors_text":"Ben Glocker, Daniel Coelho de Castro, Fabio De Sousa Ribeiro, Margherita Rosnati, Miguel Monteiro","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-03T15:19:59Z","title":"Analysing the effectiveness of a generative model for semi-supervised medical image segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.01886","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:c4b8291efab9d8f837e62d858822a686be0758c9aa338b4d6ac23a94d169eae6","target":"record","created_at":"2026-07-05T06:04:52Z","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":"e3d3c9940d14dc396613d1cbe66128bcda9f77fe1acb322f95853baf9eba6453","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-03T15:19:59Z","title_canon_sha256":"5a240e9848004b279637c42e54974bda6341b639813032c7a0545dd9f483d014"},"schema_version":"1.0","source":{"id":"2211.01886","kind":"arxiv","version":1}},"canonical_sha256":"18dfda72792895e79aba79bb77470d867e0bd3bad2d20bf719906218f503fcd5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"18dfda72792895e79aba79bb77470d867e0bd3bad2d20bf719906218f503fcd5","first_computed_at":"2026-07-05T06:04:52.386853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:04:52.386853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tEBZSI+NwMCq0ABmVZ08z6Xz97xkaiR92lmYQoaKCzIJ3KrMgcrO0pSZA6qIp8A1CzyQPQ3F+ypVu5ue4BNNAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:04:52.387331Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.01886","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4b8291efab9d8f837e62d858822a686be0758c9aa338b4d6ac23a94d169eae6","sha256:deffcda9a9d5dec2f3f3836dfe10f9a30d42429fb8ccd38911d648d568f7033b"],"state_sha256":"03e13bf0355ae998fc313066fb7302b6e4e87048decefda9bb9a0b5fff111c5d"}