{"id":"W2526511911","doi":"10.1016/j.neuroimage.2016.09.046","title":"BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":783,"is_retracted":false,"has_abstract":false,"ca_institutions":"Child and Family Research Institute; Hospital for Sick Children; University of British Columbia; SickKids Foundation; University of Toronto; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Child Health Clinician Scientist Program; BC Children's Hospital; Michael Smith Health Research BC; Child and Family Research Institute","keywords":"Convolutional neural network; Computer science; Diffusion MRI; Cognition; Artificial intelligence; Context (archaeology); Neuroimaging; Leverage (statistics); Machine learning; Pattern recognition (psychology); Neuroscience; Psychology; Medicine; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233582483473969,"score_gpt":0.2648418259143139,"score_spread":0.2425060010795742,"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."}}