{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IJ5HRYAOX6CL546W6YRTHSKMSP","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":"c4c3d8a9b8bc8e87683d79765d34f3ee098a1e1945f0752f7a8f8b0d2faa19f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-26T03:25:40Z","title_canon_sha256":"49435d6dc63fcb6c6afb482db3c0d95dd258eae73267e8714aa08149c5aa5350"},"schema_version":"1.0","source":{"id":"2603.24985","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.24985","created_at":"2026-05-25T02:01:18Z"},{"alias_kind":"arxiv_version","alias_value":"2603.24985v3","created_at":"2026-05-25T02:01:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.24985","created_at":"2026-05-25T02:01:18Z"},{"alias_kind":"pith_short_12","alias_value":"IJ5HRYAOX6CL","created_at":"2026-05-25T02:01:18Z"},{"alias_kind":"pith_short_16","alias_value":"IJ5HRYAOX6CL546W","created_at":"2026-05-25T02:01:18Z"},{"alias_kind":"pith_short_8","alias_value":"IJ5HRYAO","created_at":"2026-05-25T02:01:18Z"}],"graph_snapshots":[{"event_id":"sha256:0f3360555208fc6b92610a11edb779bc8d3005d6e82677935bce4cc95d579426","target":"graph","created_at":"2026-05-25T02:01:18Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MAML framework meta-trained on wall task together with auxiliary left and right atrial cavity tasks and boundary-aware composite loss improves segmentation over supervised fine-tuning, achieving DSC 0.64 vs 0.52 and HD95 5.70 vs 7.60 mm at 5-shot on hold-out test set."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the auxiliary cavity segmentation tasks share enough structure with the thin-wall task for effective meta-transfer, and that the chosen synthetic shift and local cohort adequately represent real clinical domain variations."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MAML with auxiliary cavity tasks and boundary-aware loss achieves better few-shot 3D left atrial wall segmentation than standard fine-tuning, reaching DSC 0.64 at 5 shots versus 0.52."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI."}],"snapshot_sha256":"5bc44cbc9ce02ef01f84bf0a0d17d835a7453e579bad0f613b29247dd867fbd4"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"eec3fcbf9cb27cc9dd35a3238cbbc7d271204fdff4ba5a6e22a0632d9676d797"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2603.24985/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Segmenting the left atrial (LA) wall from late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is challenging because of its thin geometry, low contrast, and limited expert annotations. We propose a model-agnostic meta-learning (MAML) framework with a 3D residual U-Net backbone for K-shot (K = 5, 10, 20) LA wall segmentation. The framework is meta-trained on LA wall tasks together with auxiliary LA and right atrial (RA) cavity tasks and uses a boundary-aware composite loss to improve thin-structure delineation. We evaluated MAML on a held-out clean test set and assessed its robustn","authors_text":"Calum Redpath, David Birnie, Elena Pena, Pablo Nery, Rebecca Thornhill, Robert deKemp, Sreeraman Rajan, Yusri Al-Sanaani","cross_cats":[],"headline":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-26T03:25:40Z","title":"Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.24985","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-15T00:49:00.820846Z","id":"22e590ec-7034-4889-94b8-ffb04ff628bc","model_set":{"reader":"grok-4.3"},"one_line_summary":"MAML with auxiliary cavity tasks and boundary-aware loss achieves better few-shot 3D left atrial wall segmentation than standard fine-tuning, reaching DSC 0.64 at 5 shots versus 0.52.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI.","strongest_claim":"MAML framework meta-trained on wall task together with auxiliary left and right atrial cavity tasks and boundary-aware composite loss improves segmentation over supervised fine-tuning, achieving DSC 0.64 vs 0.52 and HD95 5.70 vs 7.60 mm at 5-shot on hold-out test set.","weakest_assumption":"That the auxiliary cavity segmentation tasks share enough structure with the thin-wall task for effective meta-transfer, and that the chosen synthetic shift and local cohort adequately represent real clinical domain variations."}},"verdict_id":"22e590ec-7034-4889-94b8-ffb04ff628bc"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b9b847919d3fd43a0478f90f234699fce57d1de0280d80cb90af2d8b6291ad57","target":"record","created_at":"2026-05-25T02:01:18Z","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":"c4c3d8a9b8bc8e87683d79765d34f3ee098a1e1945f0752f7a8f8b0d2faa19f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-26T03:25:40Z","title_canon_sha256":"49435d6dc63fcb6c6afb482db3c0d95dd258eae73267e8714aa08149c5aa5350"},"schema_version":"1.0","source":{"id":"2603.24985","kind":"arxiv","version":3}},"canonical_sha256":"427a78e00ebf84bef3d6f62333c94c93cf3fe08815672091c932718a5ed794cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"427a78e00ebf84bef3d6f62333c94c93cf3fe08815672091c932718a5ed794cf","first_computed_at":"2026-05-25T02:01:18.244619Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:18.244619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yAxsJ/vbYskaMOC0swEnpf1/xj4HCI9L9epcNLhig9yMXtasG+5/vTIJYRKH6CcxEtCDNBuMRPV3fVSQUv1LDg==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:18.245363Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.24985","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9b847919d3fd43a0478f90f234699fce57d1de0280d80cb90af2d8b6291ad57","sha256:0f3360555208fc6b92610a11edb779bc8d3005d6e82677935bce4cc95d579426"],"state_sha256":"eeaa13151c4b3a2dd454a49cfcd24b3682f7fce5e757cc0c66007a994a733dc6"}