{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005630675,0.001022705,0.0007273455,0.0007670702,0.0002772107,0.0008922075,0.002087049,0.001063862,0.00364851],"category_scores_gemma":[0.0007177807,0.0005371773,0.001157956,0.0006604676,0.0003833758,0.001166826,0.001169817,0.001223329,0.001451438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261381,"about_ca_system_score_gemma":0.001736256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01808203,"about_ca_topic_score_gemma":0.02190664,"domain_scores_codex":[0.9997266,0.00003435518,0.00001537508,0.00007336069,0.00009767205,0.00005255657],"domain_scores_gemma":[0.9998666,0.00002325583,0.00001901152,0.00002284098,0.00005172424,0.00001642039],"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":[0.0003216719,0.0001657228,0.001597874,0.0003560029,0.0002439217,0.0003125288,0.00009386134,0.2427317,0.02866566,0.0178693,0.0323731,0.6752687],"study_design_scores_gemma":[0.00001477951,0.00004507639,0.0003918665,0.00001575477,0.00002052109,0.000110458,0.0000104687,0.9777248,0.007341076,0.005255715,0.009053148,0.00001627609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004378188,0.0008351681,0.9828621,0.0001941255,0.00006356882,0.00009832903,0.0006322134,0.009648772,0.001287504],"genre_scores_gemma":[0.190799,0.001889576,0.7860917,0.0006135706,0.0001098262,0.0004460734,0.005915174,0.000901644,0.01323338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01808203,"threshold_uncertainty_score":0.03595352,"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."}}