{"id":"W3122118149","doi":"10.3390/app11041675","title":"MR Images, Brain Lesions, and Deep Learning","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universitat Politècnica de València; Natural Sciences and Engineering Research Council of Canada; Universidad Técnica Particular de Loja; National Institutes of Health; Laura and John Arnold Foundation; National Science Foundation","keywords":"Computer science; Segmentation; Hyperintensity; CAD; Artificial intelligence; Multidisciplinary approach; Reliability (semiconductor); Pattern recognition (psychology); Machine learning; Magnetic resonance imaging; Medical physics; Medicine; Radiology; Engineering drawing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00160703,0.0003941209,0.0003845046,0.004916488,0.0002291263,0.001825472,0.000408575,0.0009104572,0.002130029],"category_scores_gemma":[0.006245635,0.0001423211,0.0002961315,0.004217897,0.0008643768,0.001511141,0.0005505898,0.0006139817,0.0005156231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012629,"about_ca_system_score_gemma":0.0008062277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003499072,"about_ca_topic_score_gemma":0.004239046,"domain_scores_codex":[0.9991003,0.0003119303,0.00008982074,0.0001118386,0.0003405424,0.00004558423],"domain_scores_gemma":[0.9972562,0.001770061,0.0004500559,0.00008042929,0.0003759214,0.00006726902],"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":[0.00009926733,0.00007956783,0.01387043,0.005387532,0.00028914,0.0004206526,0.0001851252,0.01166939,0.001811092,0.03962082,0.02001338,0.9065536],"study_design_scores_gemma":[0.00004157312,0.0003203581,0.09251241,0.008301737,0.0005699753,0.004051191,0.001182889,0.1367841,0.007997103,0.4381698,0.3098877,0.0001811018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.05216822,0.8084837,0.08498433,0.01667364,0.001180917,0.0001129627,0.001820056,0.0004778499,0.03409837],"genre_scores_gemma":[0.5677825,0.365072,0.04796401,0.002861571,0.002808208,0.0001219956,0.001775505,0.00006063855,0.01155371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004916488,"threshold_uncertainty_score":0.008498907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0381820082333392,"score_gpt":0.2760673389505592,"score_spread":0.23788533071722,"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."}}