{"id":"W3119217724","doi":"10.20944/preprints202101.0426.v1","title":"MRI Images, Brain Lesions and Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"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","keywords":"Computer science; Segmentation; Hyperintensity; CAD; Artificial intelligence; Deep learning; Reliability (semiconductor); Multidisciplinary approach; Machine learning; Pattern recognition (psychology); Medical physics; Magnetic resonance imaging; Medicine; Radiology","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":[0.001115058,0.0004340955,0.0003850005,0.003630463,0.0002059304,0.002017843,0.0003706807,0.0009444508,0.002399421],"category_scores_gemma":[0.005202244,0.0001474451,0.0002600317,0.003504294,0.000877758,0.001474189,0.0005359999,0.0006085847,0.0006800208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000966343,"about_ca_system_score_gemma":0.000675695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002439046,"about_ca_topic_score_gemma":0.002214035,"domain_scores_codex":[0.9992889,0.0002342228,0.00005469241,0.0001106283,0.0002694598,0.00004208978],"domain_scores_gemma":[0.9980764,0.001235046,0.0003260784,0.0000790274,0.0002293616,0.00005390475],"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":[0.0001388719,0.00009733435,0.01327091,0.004831973,0.0003017589,0.0004573127,0.0002034799,0.02414055,0.002258021,0.07988853,0.02401893,0.8503925],"study_design_scores_gemma":[0.0000320463,0.0001868215,0.05243877,0.00371635,0.0002743374,0.002563895,0.0005842643,0.1394232,0.006302464,0.5829534,0.2114135,0.0001109832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0736519,0.6824758,0.1703111,0.02062738,0.001529738,0.0001533832,0.003329528,0.001044798,0.0468763],"genre_scores_gemma":[0.663801,0.2519184,0.0576795,0.00220086,0.00261156,0.0001456875,0.00301772,0.0001196949,0.01850561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003630463,"threshold_uncertainty_score":0.008026838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306987205071668,"score_gpt":0.3440029830566599,"score_spread":0.2133042625494931,"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."}}