{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GHNRNL5WU4XA7IXGASYHOR4A3N","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":"35e6410b88c79fc707fbab99a7f5554bbf5591bfc5993483f39b56efaebb9141","cross_cats_sorted":["q-bio.TO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-12T22:21:14Z","title_canon_sha256":"4dca35ba5f96791d31aa297c637b24bfefa4d39725b8e53125482891f754add7"},"schema_version":"1.0","source":{"id":"2309.06618","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.06618","created_at":"2026-07-05T07:24:53Z"},{"alias_kind":"arxiv_version","alias_value":"2309.06618v3","created_at":"2026-07-05T07:24:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06618","created_at":"2026-07-05T07:24:53Z"},{"alias_kind":"pith_short_12","alias_value":"GHNRNL5WU4XA","created_at":"2026-07-05T07:24:53Z"},{"alias_kind":"pith_short_16","alias_value":"GHNRNL5WU4XA7IXG","created_at":"2026-07-05T07:24:53Z"},{"alias_kind":"pith_short_8","alias_value":"GHNRNL5W","created_at":"2026-07-05T07:24:53Z"}],"graph_snapshots":[{"event_id":"sha256:67574c904462e29a6ebbc6622e4cc7dbf8ad2ebab01228b47689664c770352df","target":"graph","created_at":"2026-07-05T07:24:53Z","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/2309.06618/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we introduce a novel semi-supervised learning framework tailored for medical image segmentation. Central to our approach is the innovative Multi-scale Text-aware ViT-CNN Fusion scheme. This scheme adeptly combines the strengths of both ViTs and CNNs, capitalizing on the unique advantages of both architectures as well as the complementary information in vision-language modalities. Further enriching our framework, we propose the Multi-Axis Consistency framework for generating robust pseudo labels, thereby enhancing the semisupervised learning process. Our extensive experiments on ","authors_text":"Min Xu, Yixing Lu, Zhaoxin Fan","cross_cats":["q-bio.TO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-12T22:21:14Z","title":"Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06618","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":"sha256:27ff1655cc8b56632c1090609386f40fc8983f1ce60f4c0ba50084416c735a6e","target":"record","created_at":"2026-07-05T07:24:53Z","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":"35e6410b88c79fc707fbab99a7f5554bbf5591bfc5993483f39b56efaebb9141","cross_cats_sorted":["q-bio.TO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-12T22:21:14Z","title_canon_sha256":"4dca35ba5f96791d31aa297c637b24bfefa4d39725b8e53125482891f754add7"},"schema_version":"1.0","source":{"id":"2309.06618","kind":"arxiv","version":3}},"canonical_sha256":"31db16afb6a72e0fa2e604b0774780db770d5485202dd33cf08c250146df1535","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31db16afb6a72e0fa2e604b0774780db770d5485202dd33cf08c250146df1535","first_computed_at":"2026-07-05T07:24:53.693005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:24:53.693005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uYGJo0+fxuw41/aI9A3AIaJVoFKA/+trR05Gyo+HE4ru3KuCF2fkjBno2zgtY8f3pC1u2fvwjLrPD16A+aCkCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:24:53.693480Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.06618","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27ff1655cc8b56632c1090609386f40fc8983f1ce60f4c0ba50084416c735a6e","sha256:67574c904462e29a6ebbc6622e4cc7dbf8ad2ebab01228b47689664c770352df"],"state_sha256":"2e53e19254c05543bcac7fdfe6bfd6ebf2343f574a610a7b8dbce1ab86b8205b"}