{"id":"W2742503988","doi":"10.1109/tmi.2017.2735239","title":"Significant Anatomy Detection Through Sparse Classification: A Comparative Study","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Statistical Sciences Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada","keywords":"Interpretability; Discriminative model; Artificial intelligence; Computer science; Regularization (linguistics); Pattern recognition (psychology); Univariate; Elastic net regularization; Ground truth; Image quality; Feature selection; Mathematics; Image (mathematics); Multivariate statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006238766,0.0009529663,0.0009878611,0.004242669,0.0004468384,0.001327173,0.00100214,0.001698988,0.001484804],"category_scores_gemma":[0.02436945,0.0003144545,0.001209907,0.002207514,0.001137432,0.002182445,0.0010896,0.0007308745,0.0004123146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005754782,"about_ca_system_score_gemma":0.0006160355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002733958,"about_ca_topic_score_gemma":0.002611319,"domain_scores_codex":[0.9969361,0.001282142,0.0001729646,0.0004887195,0.0009705612,0.0001495286],"domain_scores_gemma":[0.9822563,0.01252073,0.0009082205,0.001822766,0.002245632,0.0002464545],"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":[0.001889241,0.000521414,0.05565812,0.001101969,0.0007448382,0.0008048005,0.0006578593,0.1767199,0.01686923,0.00852915,0.00409927,0.7324042],"study_design_scores_gemma":[0.00005146374,0.0006517534,0.01723176,0.000103112,0.0003157941,0.001747294,0.0003699479,0.9594809,0.009905678,0.006822134,0.003265946,0.00005415646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3680167,0.005673532,0.6188148,0.000959881,0.0001122565,0.0001853646,0.0004054501,0.0009962905,0.004835662],"genre_scores_gemma":[0.8499955,0.001886235,0.1459223,0.0001122428,0.000136191,0.00005513898,0.0008692318,0.0001659506,0.0008571915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006238766,"threshold_uncertainty_score":0.03299421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07142750047562196,"score_gpt":0.3834045302446085,"score_spread":0.3119770297689865,"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."}}