{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TPOWN6AFEDYFLA7XVC4OVHUHP6","short_pith_number":"pith:TPOWN6AF","schema_version":"1.0","canonical_sha256":"9bdd66f80520f05583f7a8b8ea9e877fac401bfc75ce17f9c20072b27d345309","source":{"kind":"arxiv","id":"2308.06964","version":1},"attestation_state":"computed","paper":{"title":"How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hamza Mirza, Hassan Rivaz, Joshua Ahn, Maryse Fortin, Meagan Anstruther, Michele C. Batti\\'e, Neda Naghdi, Parinaz Roshanzamir, Yiming Xiao","submitted_at":"2023-08-14T06:40:20Z","abstract_excerpt":"Recent developments in deep learning (DL) techniques have led to great performance improvement in medical image segmentation tasks, especially with the latest Transformer model and its variants. While labels from fusing multi-rater manual segmentations are often employed as ideal ground truths in DL model training, inter-rater variability due to factors such as training bias, image noise, and extreme anatomical variability can still affect the performance and uncertainty of the resulting algorithms. Knowledge regarding how inter-rater variability affects the reliability of the resulting DL alg"},"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":false},"canonical_record":{"source":{"id":"2308.06964","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2023-08-14T06:40:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7a5b7995bc65d25756d32ffbb898da9e9940ad35cc37eeda6725c2e869faa89a","abstract_canon_sha256":"92de471f2cb35b3df0eaf258e9fe7e57cfea6bc82f9bcd2503c26dc81c7c18aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:47.790635Z","signature_b64":"8LpUjtFYLhqP6BAu2WY94c6R97JWG6jGOxLhjA4KSYTd476/vgUvUpSY4brs06cvKRVvNUkhAyF2X7EZc7QrBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bdd66f80520f05583f7a8b8ea9e877fac401bfc75ce17f9c20072b27d345309","last_reissued_at":"2026-07-05T06:40:47.790090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:47.790090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hamza Mirza, Hassan Rivaz, Joshua Ahn, Maryse Fortin, Meagan Anstruther, Michele C. Batti\\'e, Neda Naghdi, Parinaz Roshanzamir, Yiming Xiao","submitted_at":"2023-08-14T06:40:20Z","abstract_excerpt":"Recent developments in deep learning (DL) techniques have led to great performance improvement in medical image segmentation tasks, especially with the latest Transformer model and its variants. While labels from fusing multi-rater manual segmentations are often employed as ideal ground truths in DL model training, inter-rater variability due to factors such as training bias, image noise, and extreme anatomical variability can still affect the performance and uncertainty of the resulting algorithms. Knowledge regarding how inter-rater variability affects the reliability of the resulting DL alg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06964","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.06964/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":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"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":"2308.06964","created_at":"2026-07-05T06:40:47.790144+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06964v1","created_at":"2026-07-05T06:40:47.790144+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06964","created_at":"2026-07-05T06:40:47.790144+00:00"},{"alias_kind":"pith_short_12","alias_value":"TPOWN6AFEDYF","created_at":"2026-07-05T06:40:47.790144+00:00"},{"alias_kind":"pith_short_16","alias_value":"TPOWN6AFEDYFLA7X","created_at":"2026-07-05T06:40:47.790144+00:00"},{"alias_kind":"pith_short_8","alias_value":"TPOWN6AF","created_at":"2026-07-05T06:40:47.790144+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6","json":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6.json","graph_json":"https://pith.science/api/pith-number/TPOWN6AFEDYFLA7XVC4OVHUHP6/graph.json","events_json":"https://pith.science/api/pith-number/TPOWN6AFEDYFLA7XVC4OVHUHP6/events.json","paper":"https://pith.science/paper/TPOWN6AF"},"agent_actions":{"view_html":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6","download_json":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6.json","view_paper":"https://pith.science/paper/TPOWN6AF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06964&json=true","fetch_graph":"https://pith.science/api/pith-number/TPOWN6AFEDYFLA7XVC4OVHUHP6/graph.json","fetch_events":"https://pith.science/api/pith-number/TPOWN6AFEDYFLA7XVC4OVHUHP6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6/action/storage_attestation","attest_author":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6/action/author_attestation","sign_citation":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6/action/citation_signature","submit_replication":"https://pith.science/pith/TPOWN6AFEDYFLA7XVC4OVHUHP6/action/replication_record"}},"created_at":"2026-07-05T06:40:47.790144+00:00","updated_at":"2026-07-05T06:40:47.790144+00:00"}