{"id":"W4313985049","doi":"10.3389/fnins.2022.1050777","title":"A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Zhejiang Province; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; National Natural Science Foundation of China; Pfizer; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Biogen; Zhejiang Normal University; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Transfer of learning; Cognitive impairment; Computer science; Artificial intelligence; Segmentation; Disease; Alzheimer's disease; Deep learning; Cognition; Machine learning; Pattern recognition (psychology); Neuroscience; Pathology; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001214237,0.001137346,0.0008731385,0.001145664,0.0005290852,0.0006429189,0.002138641,0.0015281,0.002077934],"category_scores_gemma":[0.001593083,0.0002773918,0.001035258,0.0009476673,0.0005380182,0.00103425,0.001060848,0.001704706,0.001063093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008918857,"about_ca_system_score_gemma":0.0007983095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007233034,"about_ca_topic_score_gemma":0.00492656,"domain_scores_codex":[0.999633,0.00006959122,0.0000213778,0.0001299598,0.00007535757,0.00007079068],"domain_scores_gemma":[0.9995795,0.0001300378,0.00003508207,0.00007073674,0.0001486322,0.00003609763],"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":[0.0002794573,0.0003789249,0.002334422,0.00008094608,0.0001556804,0.0001670603,0.000117192,0.177126,0.01127603,0.001888251,0.004845997,0.80135],"study_design_scores_gemma":[0.000007535292,0.00008592253,0.0005374264,0.000006894596,0.00001623884,0.00003688032,0.00002262631,0.9938647,0.002343524,0.002585271,0.0004839964,0.000008961703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.118585,0.001641319,0.8707906,0.0006194021,0.0002524011,0.0002682809,0.0003029616,0.004208194,0.003331685],"genre_scores_gemma":[0.850178,0.0004987814,0.1417633,0.0003250096,0.000160467,0.000257035,0.0008645011,0.000114077,0.0058388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007233034,"threshold_uncertainty_score":0.01438183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08743507618567069,"score_gpt":0.3053575217799552,"score_spread":0.2179224455942845,"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."}}