{"id":"W2964249514","doi":"10.1109/bigdia.2018.8632797","title":"Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Neuroimaging; Feature selection; Artificial intelligence; Computer science; Machine learning; Magnetic resonance imaging; Classifier (UML); Functional magnetic resonance imaging; Feature (linguistics); Pattern recognition (psychology); Cognition; Psychology; Neuroscience; Medicine","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":[0.002400225,0.0005983228,0.0006553202,0.001099895,0.0002427463,0.0007564686,0.0006933996,0.0008966416,0.0005702532],"category_scores_gemma":[0.01251798,0.0001609351,0.0005417936,0.001131666,0.0009747187,0.001026174,0.0006497239,0.001186242,0.000151086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004022704,"about_ca_system_score_gemma":0.0005831843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168903,"about_ca_topic_score_gemma":0.001202424,"domain_scores_codex":[0.999262,0.000424838,0.00003621365,0.00009983379,0.0001346356,0.00004251782],"domain_scores_gemma":[0.9958965,0.003270275,0.0003866288,0.0001817572,0.0002075874,0.00005725664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002448916,0.0001951386,0.02086098,0.0005688672,0.0002057365,0.000438797,0.0002325582,0.3447894,0.005225691,0.1170181,0.00914205,0.5010778],"study_design_scores_gemma":[0.0000164089,0.0001040708,0.007585189,0.00006678509,0.00002572652,0.0002601041,0.00004501357,0.8330714,0.001730107,0.15468,0.002378609,0.00003655545],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04301552,0.007068902,0.9461444,0.002269151,0.000140047,0.00002456058,0.0002311575,0.0002072203,0.000899146],"genre_scores_gemma":[0.677108,0.007756087,0.3121266,0.0002811975,0.0006358214,0.0001665678,0.00043808,0.00007314156,0.001414484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002400225,"threshold_uncertainty_score":0.0126937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05031923078923646,"score_gpt":0.3559914082529043,"score_spread":0.3056721774636679,"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."}}