{"id":"W2900542708","doi":"10.1371/journal.pone.0225759","title":"Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Quest High Performance Computing; National Institutes of Health; Genentech; U.S. National Library of Medicine; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Northwestern University; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; National Science Foundation","keywords":"Neuroimaging; Convolutional neural network; Modality (human–computer interaction); Neuroscience; Artificial intelligence; Computer science; Medicine; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002264387,0.0001253769,0.0001576489,0.0001324275,0.0001252405,0.00005858254,0.0001710121,0.00006860136,0.0001505564],"category_scores_gemma":[0.0001582721,0.000138653,0.00004047931,0.0004676466,0.00007503795,0.0003344341,0.00005182582,0.0003217766,0.00008713863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014778,"about_ca_system_score_gemma":0.00003124106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002860563,"about_ca_topic_score_gemma":0.000005427515,"domain_scores_codex":[0.9983023,0.0002518561,0.000299446,0.0004953442,0.0003880933,0.000262939],"domain_scores_gemma":[0.9992729,0.000139975,0.0001556548,0.0003145358,0.00005057546,0.0000663618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004429045,0.0004263085,0.01971026,0.00001099842,0.000004894058,0.00000192638,0.00003813489,0.002587465,0.9744266,0.001733339,0.000005781641,0.00101006],"study_design_scores_gemma":[0.0003279281,0.00002123502,0.09985429,0.00002666132,0.00001840479,0.000006556919,0.00002601873,0.8633029,0.03608068,0.000193719,0.00001225104,0.0001292926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959496,0.00004131323,0.0009311557,0.001259367,0.0002294092,0.0004212577,0.000003741589,0.0001222988,0.001041884],"genre_scores_gemma":[0.9988605,0.0000135511,0.0001856273,0.0007303061,0.0000906135,0.00001818815,0.000007695389,0.00001994825,0.00007352339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9383458,"threshold_uncertainty_score":0.5654106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1992044012359359,"score_gpt":0.2935822954745934,"score_spread":0.09437789423865756,"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."}}