{"id":"W2050698612","doi":"10.1109/isspa.2012.6310680","title":"A novel automated approach for segmenting lateral ventricle in MR images of the brain using sparse representation classification and dictionary learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Saskatchewan Health Authority; University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Sparse approximation; Artificial intelligence; Computer science; Pattern recognition (psychology); Segmentation; Market segmentation; K-SVD; Representation (politics); Image segmentation; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001406671,0.00005545128,0.00006995376,0.0000599959,0.00004792275,0.00001367822,0.00003293639,0.00003423736,0.000001108],"category_scores_gemma":[0.00003074574,0.0000469029,0.00002104239,0.0001382171,0.00001674876,0.0001659072,0.0000244109,0.00005883733,7.395653e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002596083,"about_ca_system_score_gemma":0.000003028903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004833121,"about_ca_topic_score_gemma":6.483831e-7,"domain_scores_codex":[0.999595,0.00002388202,0.0001369091,0.00007430669,0.00005609937,0.0001138129],"domain_scores_gemma":[0.999801,0.0000449891,0.0000457307,0.00007503683,0.00002014496,0.00001303884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005600661,0.00003235274,0.07634681,0.0000224171,0.0000114741,5.65319e-8,0.0003296748,0.03708407,0.8850474,0.0001198183,0.0001430701,0.0008572296],"study_design_scores_gemma":[0.0001498196,0.000003537222,0.1193591,0.00002172987,0.000006801075,0.000006174767,0.0001468561,0.8056299,0.07460068,0.00001549169,0.00001542195,0.00004442202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8763653,0.00006257949,0.1226613,0.00002107987,0.00004261514,0.0001947215,0.000001241927,0.0002869993,0.0003641736],"genre_scores_gemma":[0.969519,0.000003530643,0.03039228,0.000008443297,0.00002641821,0.000009996876,0.000007320396,0.00001166572,0.00002131903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8104467,"threshold_uncertainty_score":0.1912645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05135255233683044,"score_gpt":0.2854290410604884,"score_spread":0.2340764887236579,"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."}}