{"id":"W4315701213","doi":"10.22489/cinc.2022.419","title":"Segmentation Uncertainty Quantification in Cardiac Propagation Models","year":2022,"lang":"en","type":"article","venue":"Computing in cardiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Dalhousie University","keywords":"Segmentation; Pipeline transport; Torso; Computer science; Pipeline (software); Eikonal equation; Computation; Image segmentation; Algorithm; Artificial intelligence; Mathematics; Engineering; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002065747,0.0006641757,0.0004849323,0.0008047359,0.000431029,0.001101838,0.0008390377,0.001108492,0.0006540146],"category_scores_gemma":[0.01226545,0.0005199349,0.0006205844,0.0004922018,0.0009404728,0.001201876,0.001086077,0.0009737979,0.0001100527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000171,"about_ca_system_score_gemma":0.0009351261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006006931,"about_ca_topic_score_gemma":0.0031573,"domain_scores_codex":[0.9992431,0.0002775299,0.00004258584,0.0001122563,0.0002607595,0.00006372009],"domain_scores_gemma":[0.992933,0.005529337,0.0006242663,0.0003507187,0.0004406457,0.0001219338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002640531,0.000006789533,0.0008093911,0.00001738437,0.00001310985,0.00003056679,0.00004366194,0.9892314,0.001364103,0.003975631,0.00007937069,0.004402187],"study_design_scores_gemma":[0.000001273419,0.000008046634,0.000149467,0.000003052297,0.000002688861,0.00001011503,0.000003340096,0.9969144,0.0006458282,0.002171613,0.00008602629,0.000004144241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1137491,0.0002118842,0.88386,0.0002491065,0.00002091075,0.00003538936,0.0001173135,0.0004318342,0.001324511],"genre_scores_gemma":[0.9355273,0.0001590677,0.06323236,0.00005038124,0.00002407867,0.00004771602,0.0001146647,0.0001565688,0.000687866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006006931,"threshold_uncertainty_score":0.01194394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1016723115243171,"score_gpt":0.3783310634152719,"score_spread":0.2766587518909548,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}