{"id":"W4386407152","doi":"10.1007/978-3-031-43898-1_44","title":"Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets","year":2023,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Lilly Endowment; Eli Lilly and Company","keywords":"Computer science; Segmentation; Artificial intelligence; Metric (unit); Dynamic contrast; Contrast (vision); Magnetic resonance imaging; Dynamic contrast-enhanced MRI; Pattern recognition (psychology); Computer vision; Cardiac magnetic resonance imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001128497,0.0008006075,0.0007056987,0.0007223289,0.0003563868,0.001067715,0.0009336335,0.0008815675,0.002586948],"category_scores_gemma":[0.00644995,0.0003702851,0.0005400837,0.0005868596,0.0004061443,0.0008357256,0.001869431,0.001052715,0.0006909029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002972338,"about_ca_system_score_gemma":0.0008436494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002843609,"about_ca_topic_score_gemma":0.005132964,"domain_scores_codex":[0.9994814,0.0001245445,0.00003488616,0.0001200774,0.00018354,0.00005562945],"domain_scores_gemma":[0.9985051,0.0008853792,0.0001294056,0.0001695897,0.0002436761,0.0000668439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001100851,0.0001993622,0.001776845,0.0003545302,0.0001884966,0.0004209015,0.0005266055,0.2709971,0.1016213,0.01144974,0.009515463,0.6018488],"study_design_scores_gemma":[0.00001101733,0.00005549431,0.0005101106,0.00001152538,0.00001266794,0.00009002627,0.0000395865,0.9781268,0.01414115,0.005069382,0.001918385,0.00001391135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006426109,0.00009362504,0.9915011,0.00004946518,0.00002299811,0.00002340138,0.00008765625,0.001537416,0.0002581921],"genre_scores_gemma":[0.3393858,0.0002458258,0.6570818,0.0001398429,0.00009420072,0.0001759789,0.0007081771,0.0008770774,0.001291383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002843609,"threshold_uncertainty_score":0.008654237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154273974724866,"score_gpt":0.3416231434608737,"score_spread":0.3261957459883871,"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."}}