{"id":"W4309246358","doi":"10.1002/hbm.26146","title":"Generative adversarial network constrained multiple loss autoencoder: A deep learning‐based individual atrophy detection for Alzheimer's disease and mild cognitive impairment","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Key Research and Development Program of China; Canadian Institutes of Health Research; National Institutes of Health; Higher Education Discipline Innovation Project; Foundation for the National Institutes of Health; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Autoencoder; Cognitive impairment; Atrophy; Cognition; Neuroscience; Generative grammar; Alzheimer's disease; Artificial intelligence; Dementia; Disease; Neuroimaging; Psychology; Adversarial system; Deep learning; Cognitive psychology; Computer science; Machine learning; Medicine; Pathology","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":[0.0009901286,0.0007823715,0.0006096817,0.0003888679,0.0001844438,0.0003757585,0.0008751678,0.0006404573,0.0007082799],"category_scores_gemma":[0.001394882,0.0003516839,0.0008114277,0.0002789874,0.0004134753,0.0005181602,0.0008503773,0.00142353,0.0002368004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005515185,"about_ca_system_score_gemma":0.00077629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008204613,"about_ca_topic_score_gemma":0.008721755,"domain_scores_codex":[0.9997225,0.00007590676,0.0000137806,0.00008384262,0.00005904125,0.00004484491],"domain_scores_gemma":[0.9997194,0.0001340131,0.00003560226,0.00002628882,0.00006515977,0.00001943582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002028702,0.0001518654,0.006203089,0.00006997185,0.000192517,0.0002053381,0.00008659252,0.8041272,0.008389839,0.002749528,0.003116204,0.174505],"study_design_scores_gemma":[0.000003430326,0.00001907994,0.0005082428,0.000005083447,0.00001178507,0.00002184005,0.000003813962,0.9977642,0.0008245146,0.0006503943,0.0001829715,0.00000469414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1067985,0.001214387,0.8877599,0.0005846755,0.00009576167,0.0000642949,0.0002371345,0.001351121,0.00189435],"genre_scores_gemma":[0.9052879,0.0006344208,0.08717417,0.0004823955,0.00005783683,0.00009951149,0.0007783349,0.000094504,0.005391032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008204613,"threshold_uncertainty_score":0.01631373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0358569893801258,"score_gpt":0.3000260737377834,"score_spread":0.2641690843576576,"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."}}