{"id":"W4220955958","doi":"10.1109/tbme.2022.3158278","title":"Localized Statistical Shape Models for Large-Scale Problems With Few Training Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Calgary Foundation; University of Calgary","keywords":"Computer science; Artificial intelligence; Kernel (algebra); USable; Machine learning; Active shape model; Kernel density estimation; Pattern recognition (psychology); Data modeling; Segmentation; Data mining; Mathematics; Estimator","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":[0.00111875,0.001020746,0.0007811383,0.0006419347,0.0003453167,0.000945651,0.001481336,0.001342435,0.001692726],"category_scores_gemma":[0.00446665,0.0006688607,0.00113344,0.0006032895,0.001207108,0.001481511,0.00157212,0.001780058,0.001179123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007976267,"about_ca_system_score_gemma":0.001168006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002967465,"about_ca_topic_score_gemma":0.002966894,"domain_scores_codex":[0.9995271,0.0001267692,0.00002370926,0.00012448,0.0001603616,0.00003762624],"domain_scores_gemma":[0.9984591,0.0007473791,0.0002529197,0.0002713122,0.0002048879,0.00006446135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006672546,0.00003759612,0.0007303635,0.00009386818,0.0000360391,0.00009560004,0.00007324941,0.9025614,0.009226788,0.01219973,0.001230427,0.07364815],"study_design_scores_gemma":[0.000001807554,0.00000820412,0.000072923,0.000003350107,0.000002533176,0.00001671476,0.000004446423,0.9948743,0.001094288,0.003478383,0.0004395694,0.000003569688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004026487,0.00006729158,0.9952178,0.00007604982,0.000007957447,0.00001106931,0.00002387434,0.0003920549,0.0001775364],"genre_scores_gemma":[0.41649,0.0006064744,0.5776585,0.0002191249,0.0001046002,0.0003009742,0.0006567173,0.0005159898,0.003447629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002967465,"threshold_uncertainty_score":0.005916595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04427984906000604,"score_gpt":0.277074279851991,"score_spread":0.2327944307919849,"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."}}