{"id":"W4309717430","doi":"10.3390/diagnostics12112888","title":"DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection","year":2022,"lang":"en","type":"article","venue":"Diagnostics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Computer science; Segmentation; Contextual image classification; Brain tumor; Binary classification; Multiclass classification; Image segmentation; Image (mathematics); Support vector machine; Medicine; Pathology","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":[0.0003112192,0.0001285357,0.0001069418,0.0001230555,0.0008597538,0.0001063959,0.0001442278,0.00003550038,0.0001385161],"category_scores_gemma":[0.00566669,0.000150162,0.00004610591,0.0003903531,0.000102777,0.0001575004,0.00005887866,0.0002393661,0.00002357621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001392339,"about_ca_system_score_gemma":0.00003383954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003716905,"about_ca_topic_score_gemma":0.000005209419,"domain_scores_codex":[0.9986183,0.0002055348,0.0002558499,0.0004425273,0.0002668049,0.0002109744],"domain_scores_gemma":[0.9969909,0.00243469,0.0002024386,0.0002323814,0.00005752519,0.00008205339],"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":[0.00008987546,0.0002069123,0.0006799929,0.00004386972,0.000004954622,0.000008081841,0.00068455,0.00005631513,0.9117687,0.05586584,0.00387776,0.02671314],"study_design_scores_gemma":[0.001189153,0.0005795193,0.02261402,0.000015505,0.00004851016,0.0001591833,0.003399658,0.02919135,0.8521633,0.04350642,0.0465981,0.0005353192],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5347902,0.00007651309,0.4543492,0.006688509,0.001500649,0.001578183,0.0002238762,0.0003756208,0.0004172565],"genre_scores_gemma":[0.9910195,0.00004136339,0.003820796,0.00368094,0.0001257967,0.001106403,0.00002728593,0.00003399164,0.0001438574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4562294,"threshold_uncertainty_score":0.6783966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687899078846778,"score_gpt":0.2973638783940729,"score_spread":0.2604848876056051,"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."}}