{"id":"W1656023708","doi":"10.1007/s10044-015-0492-0","title":"Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; University of California, San Francisco; Foundation for the National Institutes of Health","keywords":"Undersampling; Boosting (machine learning); Pattern recognition (psychology); Artificial intelligence; Segmentation; Computer science; Algorithm; Hippocampal formation; Random forest; Medicine","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":[0.002828239,0.0009139355,0.001733925,0.001781656,0.0005540293,0.0009507182,0.001280206,0.001105669,0.0007860707],"category_scores_gemma":[0.002765344,0.0008057162,0.001797068,0.0009824726,0.0005942083,0.0007555031,0.001126175,0.0008201607,0.000712014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004991302,"about_ca_system_score_gemma":0.001195978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002920378,"about_ca_topic_score_gemma":0.003307629,"domain_scores_codex":[0.9989938,0.0003526613,0.00005883021,0.0002089656,0.0002810661,0.0001047312],"domain_scores_gemma":[0.9988888,0.000416313,0.0001464911,0.0001998502,0.0002930403,0.00005558613],"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":[0.0004653979,0.0001498582,0.003689592,0.0002531511,0.0003282391,0.0002296645,0.0002395034,0.4410335,0.06212367,0.004785865,0.003570519,0.4831311],"study_design_scores_gemma":[0.00001613598,0.0000524827,0.0008064649,0.000008922105,0.00002418014,0.0001024522,0.00001068716,0.9895817,0.006029344,0.002494606,0.0008526423,0.00002032495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01551035,0.0001978724,0.9827282,0.00003461352,0.00001908065,0.00004438915,0.00003498355,0.001156132,0.0002743622],"genre_scores_gemma":[0.1759882,0.000182335,0.8225387,0.000074153,0.00004403726,0.0001166158,0.0002845509,0.000218689,0.0005528306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002920378,"threshold_uncertainty_score":0.01495737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224919150146756,"score_gpt":0.3183575597015484,"score_spread":0.2861083682000808,"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."}}