{"id":"W3176705600","doi":"10.21428/594757db.fb59ce6c","title":"Using ProtoPNet for Interpretable Alzheimer’s Disease Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","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":"Interpretability; Computer science; Artificial intelligence; Machine learning; Transparency (behavior); Architecture; Deep learning; Process (computing); Black box; Predictive modelling; Clinical Practice; Medicine; Geography","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":[0.0001711702,0.00007469297,0.00008431295,0.00003522876,0.000118739,0.0001375442,0.0003187415,0.00002896089,0.00005055691],"category_scores_gemma":[0.000170992,0.00007087694,0.00004766262,0.0001908538,0.00001110237,0.0002668854,0.0001398817,0.00008209752,0.00001415229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003299807,"about_ca_system_score_gemma":0.0002858292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003658877,"about_ca_topic_score_gemma":0.000007047097,"domain_scores_codex":[0.9990812,0.00008487966,0.0001614886,0.0003494064,0.0001293903,0.0001936495],"domain_scores_gemma":[0.9990302,0.00007906788,0.00005698131,0.0005300835,0.0001778926,0.0001258028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003791674,0.0001819412,0.05436578,0.0003072004,0.000052484,0.00002634231,0.0007195012,0.002501332,0.003070259,0.8145645,0.002865356,0.1213074],"study_design_scores_gemma":[0.00009974045,0.00001620767,0.0065234,0.00002299616,0.000009083737,0.000005028117,0.0000152499,0.9783463,0.0005438949,0.003195713,0.01112477,0.00009765608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002062557,0.0002518182,0.9902301,0.004567403,0.0002670809,0.0006373679,0.00000281327,0.0001789461,0.001801872],"genre_scores_gemma":[0.6062892,0.000002113527,0.3918946,0.0009226918,0.00007635049,0.000187109,0.00001301185,0.00001059271,0.0006043443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9758449,"threshold_uncertainty_score":0.2890278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1790745157752291,"score_gpt":0.4054451497464006,"score_spread":0.2263706339711715,"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."}}