{"id":"W2618152000","doi":"10.1109/tnb.2017.2707139","title":"Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Texture (cosmology); Pattern recognition (psychology); Artificial intelligence; Neuroimaging; Cognitive impairment; Computer science; Node (physics); Disease; Alzheimer's disease; Cognition; Feature extraction; Medicine; Psychology; Neuroscience; Image (mathematics); Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006708583,0.0006614953,0.0004968392,0.002765738,0.0003402642,0.0006451347,0.0004349596,0.0005421242,0.0007020731],"category_scores_gemma":[0.001741061,0.000213785,0.0008476659,0.001080964,0.0002444485,0.0007889087,0.0004920329,0.0004158286,0.0002306971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006900807,"about_ca_system_score_gemma":0.0003385157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01213976,"about_ca_topic_score_gemma":0.0158003,"domain_scores_codex":[0.9996754,0.00006723738,0.0000191843,0.00009937647,0.00007342811,0.00006542414],"domain_scores_gemma":[0.9994692,0.0001920594,0.0001071607,0.0000463524,0.0001342175,0.00005113134],"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":[0.001210221,0.0004174714,0.1188238,0.0001811331,0.0005316786,0.0007647386,0.0003268304,0.3113591,0.02556787,0.00341103,0.005381681,0.5320244],"study_design_scores_gemma":[0.00001089195,0.00006400794,0.01900535,0.00001623079,0.00008500669,0.0001235556,0.00005012306,0.9759135,0.001789309,0.002459387,0.000466637,0.00001604283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6638602,0.001123806,0.3290209,0.0003188185,0.00006414483,0.0001456136,0.001152117,0.0006866442,0.003627724],"genre_scores_gemma":[0.9663869,0.0002624923,0.03144613,0.00003980688,0.00003717901,0.00005357485,0.0008070949,0.00001809115,0.000948835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01213976,"threshold_uncertainty_score":0.02413821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034084285048516,"score_gpt":0.314884084730608,"score_spread":0.2745432418801228,"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."}}