{"id":"W1906917243","doi":"10.1016/j.media.2015.10.006","title":"Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; University College London Hospitals NHS Foundation Trust; Wellcome Trust; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; Cancer Research UK","keywords":"Artificial intelligence; Image registration; Percentile; Computer science; Population; Computer vision; Medical imaging; Prostate; Landmark; Magnetic resonance imaging; Ultrasound; Statistical model; Pattern recognition (psychology); Image (mathematics); Mathematics; Statistics; Medicine; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001230949,0.0001759116,0.0004438949,0.000559504,0.00006375515,0.00007426835,0.0001743131,0.0001128474,0.0001927122],"category_scores_gemma":[0.001474177,0.0001576779,0.0002826625,0.001645418,0.00007170042,0.0003181814,0.00001095134,0.0001510707,0.00002709571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001293749,"about_ca_system_score_gemma":0.00006471325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001993719,"about_ca_topic_score_gemma":0.00006679059,"domain_scores_codex":[0.9976872,0.00006935417,0.0007489142,0.0002532881,0.0009729094,0.0002682908],"domain_scores_gemma":[0.9985191,0.0001978519,0.0001264463,0.000336211,0.0003645204,0.0004559091],"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":[0.0003341666,0.001165053,0.2796738,0.001722358,0.007002817,0.00006666979,0.005291685,0.3233552,0.08839588,0.0003966606,0.1804195,0.1121762],"study_design_scores_gemma":[0.0008136205,0.00005368495,0.0120687,0.00005147752,0.0009556796,0.000002037872,0.0002173023,0.9789568,0.004935454,0.0001705641,0.001564321,0.0002103483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2046076,0.0000826898,0.7937315,0.0005280635,0.0001141759,0.0002138667,0.0001043634,0.0001970006,0.0004207874],"genre_scores_gemma":[0.9829558,0.0000298235,0.01465182,0.0001007303,0.0001638677,0.00006829167,0.001907748,0.00002467035,0.00009730139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7790797,"threshold_uncertainty_score":0.6429918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440491051959746,"score_gpt":0.2517029538067149,"score_spread":0.2372980432871174,"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."}}