{"id":"W4224438326","doi":"10.3389/fnimg.2022.832512","title":"Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroimaging","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"National Cancer Institute; National Institutes of Health; Ben and Catherine Ivy Foundation; James S. McDonnell Foundation; Mayo Clinic","keywords":"Magnetic resonance imaging; Brain tumor; Segmentation; Voxel; Medicine; Artificial intelligence; Neuroimaging; Skull; Deep learning; Computer science; Ground truth; Radiology; Nuclear medicine; Pathology; Surgery","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":[0.001356481,0.001363665,0.0007784943,0.0008389154,0.0004005694,0.0008451363,0.001411159,0.001025467,0.001699022],"category_scores_gemma":[0.004251421,0.0005492792,0.001710947,0.000503799,0.0006657667,0.000682986,0.001299948,0.001255709,0.001452837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005459696,"about_ca_system_score_gemma":0.001019391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005412917,"about_ca_topic_score_gemma":0.01003135,"domain_scores_codex":[0.9994217,0.0001473505,0.00004982183,0.0002268558,0.00008689056,0.0000674912],"domain_scores_gemma":[0.9987355,0.0004811412,0.0001634475,0.0003411187,0.0002114567,0.00006732899],"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":[0.001464191,0.0003377932,0.02780436,0.0005795526,0.0006962219,0.001889019,0.0005577586,0.4418191,0.05853705,0.001613774,0.01272492,0.4519762],"study_design_scores_gemma":[0.00004326835,0.0002411339,0.009688232,0.00006247088,0.0001087143,0.001477637,0.0001816238,0.9318056,0.04920321,0.002903702,0.004241682,0.00004269681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5897374,0.001386997,0.3899106,0.0009217849,0.0003522196,0.0004533241,0.004503611,0.009580798,0.003153353],"genre_scores_gemma":[0.8851346,0.0004089375,0.1008532,0.0003607711,0.00007111672,0.0001436801,0.009551915,0.0005365272,0.002939253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005412917,"threshold_uncertainty_score":0.01076281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516891367377414,"score_gpt":0.2259973695835429,"score_spread":0.2108284559097687,"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."}}