{"id":"W2031113857","doi":"10.1016/j.media.2011.03.004","title":"Evaluation of visualization of the prostate gland in vibro-elastography images","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Medical Research and Materiel Command; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Prostate gland; Elastography; Visualization; Prostate; Artificial intelligence; Computer vision; Computer science; Medicine; Radiology; Ultrasound; Internal medicine","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":[0.0009454069,0.0004867658,0.0002068956,0.002024708,0.0002442272,0.0008117121,0.0002498871,0.0007985929,0.003977902],"category_scores_gemma":[0.002462225,0.000282001,0.0001789087,0.0004341533,0.0002889192,0.0005866248,0.0004820622,0.0003784225,0.0002953939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000177886,"about_ca_system_score_gemma":0.000278452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001049158,"about_ca_topic_score_gemma":0.00100813,"domain_scores_codex":[0.9997888,0.00005850421,0.00002230426,0.00002578385,0.00006646722,0.0000381691],"domain_scores_gemma":[0.9985878,0.0009282003,0.00009780617,0.00005386359,0.000201776,0.0001305498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002641012,0.0001194132,0.01118307,0.00104645,0.0001296839,0.002369476,0.0006830624,0.005995217,0.8463564,0.001082947,0.0007412834,0.127652],"study_design_scores_gemma":[0.0002153304,0.001775837,0.1914639,0.0004115142,0.0005493534,0.02115031,0.0011571,0.1238926,0.6501645,0.001920116,0.007083652,0.0002158175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8457381,0.003964246,0.1409985,0.0007152488,0.00008680677,0.0002159626,0.0005522099,0.001251582,0.006477415],"genre_scores_gemma":[0.9553732,0.001029177,0.04083361,0.00009830394,0.00004325169,0.00003684668,0.0001895362,0.0002017366,0.002194246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003977902,"threshold_uncertainty_score":0.01330739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393714517619314,"score_gpt":0.3213250073869932,"score_spread":0.2973878622108,"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."}}