{"id":"W2234466563","doi":"10.1007/978-3-319-13909-8_10","title":"Confidence Weighted Local Phase Features for Robust Bone Surface Segmentation in Ultrasound","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Pelvic and Acetabular Injuries","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ground truth; Artificial intelligence; Computer vision; Segmentation; Computer science; Imaging phantom; Visualization; Image segmentation; Image quality; Image (mathematics); Radiology; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006182548,0.000386977,0.0006389468,0.0003475721,0.0001223985,0.0001108832,0.0003529674,0.0003227708,0.00004967484],"category_scores_gemma":[0.0001445884,0.0003295615,0.0001034895,0.0002378084,0.0009535872,0.000138192,0.00008947292,0.0005906404,0.00001105507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002558313,"about_ca_system_score_gemma":0.0004477993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004390585,"about_ca_topic_score_gemma":0.0002255565,"domain_scores_codex":[0.9976425,0.00002231,0.0004366811,0.0008477006,0.0005964988,0.0004543237],"domain_scores_gemma":[0.9983639,0.0006341203,0.0001972948,0.0004651429,0.0002047182,0.0001348452],"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":[0.001539164,0.0004798284,0.0009465717,0.001213359,0.0001190936,0.0004636933,0.003848819,0.07704557,0.031635,0.02751067,0.001586742,0.8536115],"study_design_scores_gemma":[0.02508449,0.005206116,0.001912739,0.00882141,0.0004581333,0.00193095,0.00002099076,0.5838345,0.1735872,0.1818182,0.01308439,0.004240818],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003109852,0.001206971,0.9929689,0.0007631168,0.0005608897,0.0008545124,0.00002318035,0.00005169643,0.0004609317],"genre_scores_gemma":[0.6592786,0.0001375595,0.3345316,0.003068088,0.0006756787,0.00001901339,0.0001699144,0.00006310165,0.002056346],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8493707,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684303318932079,"score_gpt":0.286022178079671,"score_spread":0.2691791448903502,"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."}}