{"id":"W4380483648","doi":"10.3390/bioengineering10060714","title":"AHANet: Adaptive Hybrid Attention Network for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Imaging","year":2023,"lang":"en","type":"article","venue":"Bioengineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Feature extraction; Artificial intelligence; Boosting (machine learning); Pattern recognition (psychology); Functional magnetic resonance imaging; Magnetic resonance imaging; Neuroimaging; Feature (linguistics); Neuroscience; Medicine; Psychology","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":[0.0008065039,0.001020942,0.0006877771,0.001099416,0.0005431522,0.0005573111,0.001400681,0.0009486054,0.001750654],"category_scores_gemma":[0.001198433,0.000310924,0.0007675945,0.000700041,0.0003207878,0.0009205578,0.0009443167,0.000900186,0.0004421125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009629462,"about_ca_system_score_gemma":0.0008247906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01464886,"about_ca_topic_score_gemma":0.01993745,"domain_scores_codex":[0.9997763,0.00004073822,0.00001307246,0.00007752411,0.00004167198,0.00005064932],"domain_scores_gemma":[0.9997347,0.0001000984,0.00002336417,0.00002261248,0.00009409674,0.00002510887],"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":[0.00087208,0.000766299,0.009013095,0.0002486867,0.0004971842,0.0004631029,0.0001571533,0.1999014,0.01801307,0.002856619,0.02148481,0.7457266],"study_design_scores_gemma":[0.0000423907,0.0001924652,0.001784736,0.00002015792,0.00009462375,0.0001427045,0.00003046848,0.9876252,0.004412663,0.003390781,0.002244692,0.00001919608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3022136,0.006495155,0.6642811,0.001837848,0.0009431401,0.0006004677,0.002204333,0.009463071,0.01196126],"genre_scores_gemma":[0.8632196,0.001176953,0.1205254,0.0007937246,0.000288202,0.0003260105,0.002908101,0.000117516,0.01064442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01464886,"threshold_uncertainty_score":0.02912718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07824427379776605,"score_gpt":0.2837317822626899,"score_spread":0.2054875084649238,"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."}}