{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IJ5HRYAOX6CL546W6YRTHSKMSP","short_pith_number":"pith:IJ5HRYAO","schema_version":"1.0","canonical_sha256":"427a78e00ebf84bef3d6f62333c94c93cf3fe08815672091c932718a5ed794cf","source":{"kind":"arxiv","id":"2603.24985","version":3},"attestation_state":"computed","paper":{"title":"Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Calum Redpath, David Birnie, Elena Pena, Pablo Nery, Rebecca Thornhill, Robert deKemp, Sreeraman Rajan, Yusri Al-Sanaani","submitted_at":"2026-03-26T03:25:40Z","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":true},"canonical_record":{"source":{"id":"2603.24985","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-26T03:25:40Z","cross_cats_sorted":[],"title_canon_sha256":"49435d6dc63fcb6c6afb482db3c0d95dd258eae73267e8714aa08149c5aa5350","abstract_canon_sha256":"c4c3d8a9b8bc8e87683d79765d34f3ee098a1e1945f0752f7a8f8b0d2faa19f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:18.245363Z","signature_b64":"yAxsJ/vbYskaMOC0swEnpf1/xj4HCI9L9epcNLhig9yMXtasG+5/vTIJYRKH6CcxEtCDNBuMRPV3fVSQUv1LDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427a78e00ebf84bef3d6f62333c94c93cf3fe08815672091c932718a5ed794cf","last_reissued_at":"2026-05-25T02:01:18.244619Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:18.244619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Calum Redpath, David Birnie, Elena Pena, Pablo Nery, Rebecca Thornhill, Robert deKemp, Sreeraman Rajan, Yusri Al-Sanaani","submitted_at":"2026-03-26T03:25:40Z","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"},"claims":{"count":4,"items":[{"kind":"strongest_claim","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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5bc44cbc9ce02ef01f84bf0a0d17d835a7453e579bad0f613b29247dd867fbd4"},"source":{"id":"2603.24985","kind":"arxiv","version":3},"verdict":{"id":"22e590ec-7034-4889-94b8-ffb04ff628bc","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T00:49:00.820846Z","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.","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","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.","pith_extraction_headline":"Meta-learning with auxiliary cavity tasks improves few-shot accuracy for thin left atrial wall segmentation in 3D MRI."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.24985/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"eec3fcbf9cb27cc9dd35a3238cbbc7d271204fdff4ba5a6e22a0632d9676d797"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2603.24985","created_at":"2026-05-25T02:01:18.244747+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.24985v3","created_at":"2026-05-25T02:01:18.244747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.24985","created_at":"2026-05-25T02:01:18.244747+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ5HRYAOX6CL","created_at":"2026-05-25T02:01:18.244747+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ5HRYAOX6CL546W","created_at":"2026-05-25T02:01:18.244747+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ5HRYAO","created_at":"2026-05-25T02:01:18.244747+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP","json":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP.json","graph_json":"https://pith.science/api/pith-number/IJ5HRYAOX6CL546W6YRTHSKMSP/graph.json","events_json":"https://pith.science/api/pith-number/IJ5HRYAOX6CL546W6YRTHSKMSP/events.json","paper":"https://pith.science/paper/IJ5HRYAO"},"agent_actions":{"view_html":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP","download_json":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP.json","view_paper":"https://pith.science/paper/IJ5HRYAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.24985&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ5HRYAOX6CL546W6YRTHSKMSP/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ5HRYAOX6CL546W6YRTHSKMSP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP/action/storage_attestation","attest_author":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP/action/author_attestation","sign_citation":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP/action/citation_signature","submit_replication":"https://pith.science/pith/IJ5HRYAOX6CL546W6YRTHSKMSP/action/replication_record"}},"created_at":"2026-05-25T02:01:18.244747+00:00","updated_at":"2026-05-25T02:01:18.244747+00:00"}