{"id":"W4407668521","doi":"10.3389/fninf.2025.1527582","title":"Contrastive self-supervised learning for neurodegenerative disorder classification","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of California, San Francisco; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Deutsche Forschungsgemeinschaft; 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; National Institute on Aging; Alzheimer's Association","keywords":"Artificial intelligence; Computer science; Frontotemporal dementia; Feature (linguistics); Pattern recognition (psychology); Machine learning; Perceptron; Frontotemporal lobar degeneration; Neuroimaging; Extractor; Dementia; Artificial neural network; Disease; Psychology; Medicine; Neuroscience; Pathology","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":[0.003916366,0.001209072,0.001071431,0.0009942089,0.0004148963,0.0005856362,0.001599722,0.001317284,0.001428748],"category_scores_gemma":[0.006708199,0.0003585875,0.001052664,0.0006694091,0.0009687025,0.0007549551,0.0009228988,0.002021847,0.0008381612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013965,"about_ca_system_score_gemma":0.001057664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003774924,"about_ca_topic_score_gemma":0.003921048,"domain_scores_codex":[0.9985551,0.0006044122,0.0000787841,0.0004350222,0.000226093,0.0001006255],"domain_scores_gemma":[0.9968034,0.001600932,0.0002947192,0.000563344,0.0006184427,0.0001190955],"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":[0.0008139919,0.0007040104,0.01067548,0.000287854,0.000432091,0.000205372,0.000145929,0.5710393,0.006769333,0.004316244,0.01315136,0.391459],"study_design_scores_gemma":[0.00001278861,0.00005136471,0.0005786692,0.00000904777,0.000009513429,0.00001860193,0.000006321043,0.9946557,0.001186039,0.003119612,0.0003461987,0.00000611056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1644908,0.003921616,0.8187385,0.001192319,0.0002601064,0.0003813554,0.001468599,0.006690609,0.002856083],"genre_scores_gemma":[0.8794572,0.0003313443,0.1132916,0.0003955499,0.0001640018,0.0002840168,0.002901456,0.0001752569,0.002999606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003916366,"threshold_uncertainty_score":0.02071196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216786907003842,"score_gpt":0.2899944390999336,"score_spread":0.2778265700298951,"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."}}