{"id":"W4241787440","doi":"10.1007/978-3-030-12029-0","title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","year":2019,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of Toronto","funders":"","keywords":"Computer science; Segmentation; Computational model; Artificial intelligence; Statistical model; Ventricle; Data science; Cardiology; Medicine","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":[0.0008395179,0.0002318399,0.0003333877,0.0002747063,0.0001288335,0.0002126808,0.0008546811,0.0001484793,0.000003770175],"category_scores_gemma":[0.0001567878,0.0001786235,0.00004854019,0.0003083989,0.0009943072,0.0004784394,0.0006720949,0.0003085655,0.000001989372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167956,"about_ca_system_score_gemma":0.0005735145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001611575,"about_ca_topic_score_gemma":0.00001116621,"domain_scores_codex":[0.9973572,0.000158343,0.0004629605,0.0008363183,0.0009547692,0.000230332],"domain_scores_gemma":[0.9975423,0.001360068,0.0002935862,0.0005187324,0.0001983451,0.00008699317],"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":[0.00001233549,0.00003797982,0.0001063429,0.0002851123,0.00001995131,0.000003133548,0.001729916,0.04283078,0.000585971,0.03276362,0.0002973149,0.9213275],"study_design_scores_gemma":[0.0003174586,0.000135308,0.0009259445,0.0002347657,0.00001143659,0.00002728968,0.000001067788,0.7751007,0.002314552,0.2206868,0.00002331591,0.0002213152],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001999369,0.0008701809,0.9965717,0.001108611,0.0004831104,0.0005991154,0.00001606891,0.00005272111,0.00009857874],"genre_scores_gemma":[0.07557431,0.0002431364,0.9233695,0.0005590005,0.0001334423,0.00001018077,0.0000195725,0.00001479402,0.00007601316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9211062,"threshold_uncertainty_score":0.7284055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0361692646105382,"score_gpt":0.2995240711803284,"score_spread":0.2633548065697902,"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."}}