{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002199456,0.0008226309,0.0008490604,0.001482394,0.0004170652,0.001093714,0.0007292753,0.0009504125,0.00101034],"category_scores_gemma":[0.003014581,0.000270147,0.0009017118,0.001029805,0.0004392994,0.001239024,0.001036539,0.001023393,0.0004398553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037043,"about_ca_system_score_gemma":0.0009678685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0107823,"about_ca_topic_score_gemma":0.01298286,"domain_scores_codex":[0.9993861,0.0001562983,0.00003454474,0.0001384546,0.0001359521,0.000148702],"domain_scores_gemma":[0.9993688,0.0002527842,0.00007358264,0.00009199822,0.0001618992,0.00005086403],"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":[0.001275258,0.0005393628,0.03149052,0.0001864818,0.0006972263,0.0003880726,0.0002542857,0.189524,0.01543493,0.006061751,0.007435109,0.7467129],"study_design_scores_gemma":[0.00001535121,0.00008910762,0.006244597,0.00004113265,0.0001010577,0.0001294934,0.00007510644,0.9787362,0.006063202,0.007019838,0.001459816,0.00002502234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6042657,0.007889785,0.3750129,0.001895096,0.0003360164,0.0001363347,0.0011368,0.002775115,0.006552283],"genre_scores_gemma":[0.96113,0.0006026042,0.03577138,0.0001228359,0.0000708909,0.00002737,0.0006735524,0.00003539247,0.001566016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0107823,"threshold_uncertainty_score":0.02143908,"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."}}