{"id":"W2590760288","doi":"10.1016/j.knosys.2017.02.025","title":"Automatic computation of regions of interest by robust principal component analysis. Application to automatic dementia diagnosis","year":2017,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Pfizer; Novartis Pharmaceuticals Corporation; Ministerio de Ciencia e Innovación; F. Hoffmann-La Roche; GE Healthcare; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Takeda Pharmaceutical Company; Eli Lilly and Company; Bristol-Myers Squibb; Merck","keywords":"Computer science; Principal component analysis; Computation; Dementia; Component (thermodynamics); Artificial intelligence; Data mining; Pattern recognition (psychology); Algorithm; Medicine; 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":[0.0008483588,0.000805687,0.0009007324,0.002353061,0.0005384119,0.001162842,0.0008632828,0.001033274,0.002738533],"category_scores_gemma":[0.003669952,0.00052956,0.0008716646,0.001488092,0.0004564497,0.0006349875,0.0007596619,0.0008748336,0.001379937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003422311,"about_ca_system_score_gemma":0.001017849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004914601,"about_ca_topic_score_gemma":0.004702955,"domain_scores_codex":[0.9995648,0.0001255667,0.00003160653,0.00008362322,0.0001406431,0.00005379939],"domain_scores_gemma":[0.9991987,0.0003708686,0.00008072186,0.00008793442,0.0002301066,0.00003156285],"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":[0.0004275271,0.0001299362,0.001222389,0.0004205957,0.0001294054,0.000252717,0.0001375503,0.02229398,0.1266198,0.002829085,0.00636099,0.8391761],"study_design_scores_gemma":[0.0001091814,0.0002069371,0.01368847,0.0001232705,0.0002875084,0.001666432,0.0002490799,0.794045,0.1630492,0.01486302,0.01159077,0.0001210464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01901415,0.001894693,0.9746633,0.0002464566,0.00006748983,0.0001229364,0.0003296911,0.002703467,0.0009578604],"genre_scores_gemma":[0.1865975,0.001171195,0.809429,0.00008982635,0.00005748085,0.0001816106,0.000518855,0.00036858,0.001585839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004914601,"threshold_uncertainty_score":0.009772003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05652803893087573,"score_gpt":0.3291563381050803,"score_spread":0.2726282991742046,"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."}}