{"id":"W3180552095","doi":"10.3390/a14070212","title":"Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?","year":2021,"lang":"en","type":"article","venue":"Algorithms","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ground truth; Deep learning; Segmentation; Hausdorff distance; Artificial neural network; Generalization; Data set; Interpretability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001358542,0.0001464769,0.0003087537,0.00006874985,0.0001080308,0.00006553935,0.00003582549,0.00006912306,0.0003252772],"category_scores_gemma":[0.000283083,0.0001464745,0.0002162986,0.0002716737,0.00004047873,0.00006006454,0.00003388262,0.0001930774,0.0001213019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007454589,"about_ca_system_score_gemma":0.0001364698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000253809,"about_ca_topic_score_gemma":7.423772e-7,"domain_scores_codex":[0.9988629,0.00008655982,0.0001800957,0.0002853055,0.0003317583,0.0002533463],"domain_scores_gemma":[0.9990208,0.0002881498,0.00004175843,0.0002831562,0.000198913,0.0001672621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001087209,0.0005492207,0.2335778,0.0002812841,0.0007112889,0.003802863,0.004992366,0.005895831,0.01028931,0.000132743,0.05834889,0.6813097],"study_design_scores_gemma":[0.01068105,0.0004972056,0.03426609,0.0008706246,0.001479221,0.0005996844,0.02865797,0.07511066,0.1207548,0.0001648733,0.7255014,0.001416428],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05282728,0.1747413,0.6228602,0.0623287,0.01454287,0.002037862,0.00007862668,0.00209865,0.06848456],"genre_scores_gemma":[0.7964686,0.005566507,0.1702283,0.006748217,0.005653657,0.0002585928,0.001129795,0.0002589942,0.01368736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7436413,"threshold_uncertainty_score":0.5973056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184151105983421,"score_gpt":0.2759254271493967,"score_spread":0.2640839160895625,"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."}}