{"id":"W2891479923","doi":"10.3390/forecast1010005","title":"Improved Brain Tumor Segmentation via Registration-Based Brain Extraction","year":2018,"lang":"en","type":"article","venue":"Forecasting","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton; University of Alberta","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Skull; Fluid-attenuated inversion recovery; False positive paradox; Computer vision; Volume (thermodynamics); Pattern recognition (psychology); Process (computing); Image segmentation; Nuclear medicine; Medicine; Magnetic resonance imaging; Radiology; Anatomy","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":[0.001293355,0.001317436,0.001653315,0.003431612,0.0005185372,0.001481698,0.001631379,0.001175957,0.004399163],"category_scores_gemma":[0.003460957,0.000892034,0.001441648,0.002203866,0.0004071429,0.001611385,0.001186968,0.0008630801,0.003877065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007022269,"about_ca_system_score_gemma":0.001189126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004670165,"about_ca_topic_score_gemma":0.009409962,"domain_scores_codex":[0.9985098,0.0002850243,0.0001346496,0.0004111622,0.0005426274,0.0001166461],"domain_scores_gemma":[0.9981942,0.0005352487,0.0002136875,0.000542272,0.0004768102,0.00003786513],"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":[0.0003392241,0.0001490737,0.003517032,0.000244132,0.0002800902,0.0002712136,0.0002036402,0.03269409,0.1941727,0.00187271,0.00862818,0.7576279],"study_design_scores_gemma":[0.0001361264,0.0003134907,0.01831901,0.00003639205,0.000366896,0.00271149,0.0001035073,0.6785199,0.2730747,0.004741184,0.02148711,0.0001902411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06563366,0.0006742444,0.9113505,0.0003080321,0.00009537626,0.0001680571,0.0004911472,0.01897111,0.002307777],"genre_scores_gemma":[0.1775699,0.0004593002,0.8142605,0.0001976945,0.0001256855,0.0001364527,0.00129815,0.001548696,0.004403567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004670165,"threshold_uncertainty_score":0.01471663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0603655812904097,"score_gpt":0.3001008746147609,"score_spread":0.2397352933243512,"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."}}