{"id":"W1884191083","doi":"10.1016/j.media.2016.05.004","title":"Brain tumor segmentation with Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3245,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Convolutional neural network; Exploit; Artificial intelligence; Segmentation; Deep learning; Set (abstract data type); Deep neural networks; Pattern recognition (psychology); Layer (electronics); Machine learning; Architecture; Artificial neural network","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":[0.0004789373,0.0009959874,0.0006509791,0.001208496,0.0003233526,0.001098755,0.0008070405,0.001253076,0.001981628],"category_scores_gemma":[0.001216426,0.0006310004,0.0007675856,0.000988337,0.0003401395,0.0007473425,0.0008583586,0.001023227,0.001041644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008736411,"about_ca_system_score_gemma":0.001041078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00753408,"about_ca_topic_score_gemma":0.01287734,"domain_scores_codex":[0.9998162,0.00002470233,0.00001233999,0.00006055961,0.00005180373,0.00003429442],"domain_scores_gemma":[0.9996799,0.0001171232,0.000048994,0.00004550484,0.00008657639,0.00002194698],"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":[0.0003051189,0.0001214677,0.002101664,0.0001957404,0.000146288,0.0001586742,0.00006752598,0.2036464,0.03240368,0.005889496,0.0081811,0.7467827],"study_design_scores_gemma":[0.000004986069,0.00001833448,0.0004919621,0.00001507811,0.00002114222,0.00008029729,0.000009933105,0.9830475,0.01004803,0.004877128,0.001378226,0.000007455632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02367568,0.001081716,0.9697276,0.0004387953,0.00007689104,0.00007161322,0.0003414352,0.002454396,0.002131879],"genre_scores_gemma":[0.4869321,0.001399075,0.4979886,0.0004903125,0.0001486584,0.0001614306,0.001228409,0.0004572513,0.0111942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00753408,"threshold_uncertainty_score":0.01498049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320400802125175,"score_gpt":0.2671777228647449,"score_spread":0.2539737148434931,"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."}}