{"id":"W3201318749","doi":"10.3389/fnins.2021.653213","title":"Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Mental Health; York University; Johns Hopkins University; Washington University in St. Louis; University of Alberta; University of Washington; National Institutes of Health; University of Minnesota; Children's Hospital of Philadelphia; Autism Speaks","keywords":"Computer science; Artificial intelligence; Contrast (vision); Neuroimaging; Perception; Pattern recognition (psychology); Adversarial system; Machine learning; Computer vision; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002952986,0.0001608549,0.0002594829,0.0001424979,0.000177552,0.0001284259,0.0005367694,0.00004772222,0.000006000508],"category_scores_gemma":[0.0003211823,0.0001422747,0.00005847408,0.0009085464,0.0003322593,0.0009693957,0.0002101931,0.0002521683,0.000001001359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117405,"about_ca_system_score_gemma":0.0001810911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003882609,"about_ca_topic_score_gemma":0.00001735947,"domain_scores_codex":[0.998095,0.0002091623,0.0002661914,0.0006839318,0.0003883271,0.0003574166],"domain_scores_gemma":[0.9992747,0.00004695916,0.0001269397,0.000325689,0.000140233,0.00008545378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008477288,0.0003739957,0.6179706,0.00002535621,0.00001815203,0.0003110895,0.001913715,0.1676639,0.1942974,0.0002847599,0.001788057,0.01526818],"study_design_scores_gemma":[0.001174076,0.0002917195,0.3495625,0.0000555666,0.00001334265,0.0000472692,0.0004009102,0.6175021,0.02978958,0.00003179441,0.0009041615,0.0002269951],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03806341,0.0001284475,0.9594591,0.0001671609,0.001842533,0.0001151836,0.00000771236,0.00004447956,0.0001719866],"genre_scores_gemma":[0.8690658,0.00007081453,0.1305245,0.00006841171,0.00006583036,0.000006846993,0.000001658742,0.000007441371,0.0001886345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8310024,"threshold_uncertainty_score":0.5801793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611834406027478,"score_gpt":0.2193894717269557,"score_spread":0.203271127666681,"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."}}