{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TRQ6Q2DTFXS7NCXME47S447ALO","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":"f4a13b639f60967fd2fc003e2a3b67cc48bbcd689a1d22c798f4af69bc16cdfd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-21T16:32:48Z","title_canon_sha256":"c4db6a482e95b0bda9cb44bc1d0fc19f4f9f4bc70d2511e6e8d6764939da3282"},"schema_version":"1.0","source":{"id":"2507.15777","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.15777","created_at":"2026-05-22T01:03:44Z"},{"alias_kind":"arxiv_version","alias_value":"2507.15777v3","created_at":"2026-05-22T01:03:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.15777","created_at":"2026-05-22T01:03:44Z"},{"alias_kind":"pith_short_12","alias_value":"TRQ6Q2DTFXS7","created_at":"2026-05-22T01:03:44Z"},{"alias_kind":"pith_short_16","alias_value":"TRQ6Q2DTFXS7NCXM","created_at":"2026-05-22T01:03:44Z"},{"alias_kind":"pith_short_8","alias_value":"TRQ6Q2DT","created_at":"2026-05-22T01:03:44Z"}],"graph_snapshots":[{"event_id":"sha256:319a7b682cc9f3dfd964f1f09c3077b90325a2a9cf95edcde758605fb5bd59c4","target":"graph","created_at":"2026-05-22T01:03:44Z","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/2507.15777/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, guiding real-time intra-operative navigation, and supporting precise post-operative assessment. However, commonly used learning methods for medical and surgical imaging segmentation tasks penalise all errors equivalently and thus fail to exploit any inter-class semantics in the label space. This becomes particularly problematic as the cardinality and richness of labels increases to include subtly different classes. In","authors_text":"Aaron Kujawa, Jonathan Shapey, Junwen Wang, Oscar MacCormac, Tom Vercauteren, William Rochford","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-21T16:32:48Z","title":"Label tree semantic losses for rich multi-class medical image segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.15777","kind":"arxiv","version":3},"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":"sha256:572c25235bcc5a4ec72ca23640254069e5b15121dfcafdc5468452a4748822d4","target":"record","created_at":"2026-05-22T01:03:44Z","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":"f4a13b639f60967fd2fc003e2a3b67cc48bbcd689a1d22c798f4af69bc16cdfd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-21T16:32:48Z","title_canon_sha256":"c4db6a482e95b0bda9cb44bc1d0fc19f4f9f4bc70d2511e6e8d6764939da3282"},"schema_version":"1.0","source":{"id":"2507.15777","kind":"arxiv","version":3}},"canonical_sha256":"9c61e868732de5f68aec273f2e73e05b9f7afc03f83afc1e691a974931a8644a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c61e868732de5f68aec273f2e73e05b9f7afc03f83afc1e691a974931a8644a","first_computed_at":"2026-05-22T01:03:44.282499Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:03:44.282499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9NnflvaBjLYNdRZj+oqsYUqbWKVQ3mlN7B/6zkz1EWOOjkeyjAG3VDLpdcISWF+EvbLab+xqA3DHql0nfgl+AQ==","signature_status":"signed_v1","signed_at":"2026-05-22T01:03:44.283429Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.15777","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:572c25235bcc5a4ec72ca23640254069e5b15121dfcafdc5468452a4748822d4","sha256:319a7b682cc9f3dfd964f1f09c3077b90325a2a9cf95edcde758605fb5bd59c4"],"state_sha256":"405fa6450c8ac283c77b6234772ddb689df23d7d5981952b07d15b894f4f1ccb"}