{"id":"W2103856694","doi":"10.1109/iembs.2007.4352426","title":"2D/3D Registration of Multiple Bones","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Kingston General Hospital","funders":"","keywords":"Computer vision; Artificial intelligence; Image registration; Radiography; Wrist; Computer science; Object (grammar); Carpal bones; Matching (statistics); Image (mathematics); Medicine; Anatomy; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001596538,0.00081503,0.0008377112,0.001291821,0.0004495438,0.001399518,0.001018282,0.001094715,0.003452214],"category_scores_gemma":[0.0039891,0.0009019996,0.001076318,0.001342905,0.001182608,0.001351619,0.002514291,0.0008059676,0.001958812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003662425,"about_ca_system_score_gemma":0.0008406029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558922,"about_ca_topic_score_gemma":0.001975681,"domain_scores_codex":[0.9975377,0.0004453361,0.000144798,0.0006124289,0.001140676,0.000118997],"domain_scores_gemma":[0.9987901,0.0002464975,0.0001742881,0.0005594956,0.0001897206,0.00004000625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005092443,0.0001322296,0.005814136,0.0004625357,0.0001910407,0.001139756,0.0005045079,0.1226402,0.303138,0.02022253,0.004089204,0.5411566],"study_design_scores_gemma":[0.0000772919,0.0004120826,0.008930616,0.00009999179,0.0001054938,0.005932026,0.0003726612,0.6415399,0.289146,0.02174178,0.03146702,0.0001750745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03076516,0.0002918554,0.9653751,0.0001027799,0.00008459864,0.00008589605,0.0001396156,0.0008726415,0.002282393],"genre_scores_gemma":[0.3292016,0.0004131094,0.6659916,0.0001397145,0.00005014866,0.0001315145,0.0004236807,0.0003679363,0.003280608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003452214,"threshold_uncertainty_score":0.01154882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063440627836565,"score_gpt":0.2400542496406699,"score_spread":0.2194198433623042,"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."}}