{"id":"W3130934179","doi":"","title":"Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV (segmentation; shape models and landmark detection)","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Landmark; Computer science; Segmentation; Intervention (counseling); Computer vision; Computer graphics (images); Artificial intelligence; Cartography; Multimedia; Medical physics; Medicine; Geography; Nursing","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":[0.002806159,0.001625551,0.001256144,0.00235514,0.0006909602,0.002349909,0.001324383,0.001374462,0.02498317],"category_scores_gemma":[0.003548297,0.0005736448,0.0006201418,0.002127623,0.001129785,0.001442736,0.001942478,0.001486061,0.01116404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534485,"about_ca_system_score_gemma":0.003305857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401601,"about_ca_topic_score_gemma":0.01559953,"domain_scores_codex":[0.9990886,0.0001903922,0.00004949102,0.0001818172,0.0003844169,0.0001051503],"domain_scores_gemma":[0.998435,0.0003042496,0.00003881598,0.0001577375,0.0007980431,0.0002661828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005022177,0.0001894953,0.000785479,0.0004037646,0.0001042185,0.00007816071,0.00005896774,0.003190572,0.006518109,0.008073371,0.6501912,0.3299045],"study_design_scores_gemma":[0.0001188541,0.000280728,0.01401957,0.0003073927,0.000146749,0.0005830693,0.0001476356,0.08213252,0.02257238,0.01749871,0.8621092,0.00008319002],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04315525,0.1339213,0.5238853,0.03867532,0.04685537,0.00125465,0.01825478,0.01779926,0.1761988],"genre_scores_gemma":[0.1049668,0.06145613,0.2635246,0.001635356,0.008526379,0.0007289445,0.03233229,0.004305773,0.5225238],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02498317,"threshold_uncertainty_score":0.08357704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945852769710798,"score_gpt":0.2953260558606267,"score_spread":0.2658675281635187,"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."}}