{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003491381,0.00006690557,0.00006762135,0.00005210861,0.0007122947,0.000179089,0.0001324838,0.00002713606,0.0001222497],"category_scores_gemma":[0.0005500054,0.00005964734,0.00001576815,0.0005803022,0.0003983926,0.0001166368,0.00006370433,0.0001288231,0.00007727652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001006,"about_ca_system_score_gemma":0.00004129659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002618104,"about_ca_topic_score_gemma":0.000006233208,"domain_scores_codex":[0.9989702,0.0000853693,0.0001042425,0.0004343582,0.0002326533,0.0001731461],"domain_scores_gemma":[0.9994452,0.0003266857,0.00005099031,0.0001004995,0.00001341683,0.00006323363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001626551,0.00001117305,0.000124374,0.000002581233,2.786124e-7,0.000003888401,0.000124825,0.00001764181,0.9158568,0.03159168,0.000202478,0.0520626],"study_design_scores_gemma":[0.0001750246,0.0000286068,0.005633785,0.00000488594,0.000002536475,0.00007187553,0.001673462,0.001722802,0.9528159,0.003345885,0.03437293,0.000152307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8378937,0.0003436124,0.003518958,0.01020195,0.000300367,0.0001850531,0.000001628752,0.0002927844,0.147262],"genre_scores_gemma":[0.9959847,0.00007024168,0.000554478,0.001726598,0.00002950729,0.00001237597,5.554895e-7,0.000004436586,0.001617052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1580911,"threshold_uncertainty_score":0.5478467,"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."}}