{"id":"W4406880995","doi":"10.48550/arxiv.2501.15733","title":"Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Disease; Transformer; Medicine; Neuroscience; Computer science; Artificial intelligence; Computer vision; Psychology; Pathology; Engineering","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":[0.000821229,0.00105354,0.0005669773,0.001150587,0.0002353819,0.000859678,0.001099437,0.001016805,0.00118196],"category_scores_gemma":[0.002317119,0.0003133409,0.0008035945,0.0004561043,0.0003396117,0.001039162,0.000850199,0.0008640849,0.0007553711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006822766,"about_ca_system_score_gemma":0.000921629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00833216,"about_ca_topic_score_gemma":0.01382417,"domain_scores_codex":[0.9997109,0.0000466045,0.00001663365,0.00009390337,0.00008685306,0.00004500766],"domain_scores_gemma":[0.9997101,0.00008591385,0.00003532282,0.00004104414,0.0001044098,0.00002321815],"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":[0.0007809558,0.0003711181,0.01244629,0.0002985754,0.0002821113,0.0008016587,0.0001093782,0.1584874,0.04201289,0.002938486,0.01224079,0.7692304],"study_design_scores_gemma":[0.00002170361,0.000125214,0.002514837,0.00003087041,0.00007990593,0.0005646702,0.00002711283,0.9723083,0.01940661,0.003020566,0.001875071,0.00002510642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2606587,0.005030079,0.7123718,0.001661673,0.0004071992,0.0003165296,0.002008158,0.01045764,0.00708827],"genre_scores_gemma":[0.8833106,0.001290954,0.1091944,0.0005602929,0.0001072475,0.00005606294,0.002037955,0.0001147845,0.003327742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00833216,"threshold_uncertainty_score":0.01656735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08708295928670468,"score_gpt":0.3243987563784414,"score_spread":0.2373157970917367,"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."}}