{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009038106,0.0008476606,0.000345882,0.0006215465,0.000362242,0.001085901,0.001026547,0.000984301,0.003907942],"category_scores_gemma":[0.004428704,0.0003678444,0.000570945,0.0004489466,0.0004470398,0.001686899,0.0007169457,0.001557934,0.001174966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009600876,"about_ca_system_score_gemma":0.001026276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008314914,"about_ca_topic_score_gemma":0.01446732,"domain_scores_codex":[0.9997514,0.00006451875,0.00001583104,0.00007878984,0.00005383479,0.00003554345],"domain_scores_gemma":[0.9987079,0.0007392496,0.0000915489,0.0001750061,0.0002419275,0.00004433666],"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":[0.0007224542,0.0003548957,0.0151687,0.0003305888,0.000173299,0.0009385546,0.0002008225,0.7113773,0.007439153,0.02054086,0.01914967,0.2236038],"study_design_scores_gemma":[0.00001186346,0.00003666971,0.0005150092,0.00002450658,0.0000128657,0.00006637019,0.0000214624,0.9808103,0.0029487,0.0135491,0.001995475,0.000007625467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2327326,0.001005811,0.7232,0.003618713,0.0005727328,0.000248651,0.006270812,0.01722306,0.0151277],"genre_scores_gemma":[0.8539982,0.000435771,0.1337461,0.000733254,0.00006155782,0.0001349904,0.005564384,0.0003031711,0.005022572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008314914,"threshold_uncertainty_score":0.01653302,"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."}}