{"id":"W3142204669","doi":"10.1016/j.media.2021.102245","title":"A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Regularization (linguistics); Imaging phantom; Computer science; Parametric statistics; Mathematics; Algorithm; Artificial intelligence; Pattern recognition (psychology); Physics; Statistics; Optics","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":[0.002169987,0.0006863088,0.0005568144,0.001567742,0.0001944101,0.0007785158,0.0008025798,0.0007893171,0.0009088321],"category_scores_gemma":[0.002869786,0.0004414709,0.0006378326,0.0009094003,0.0006443826,0.0007953194,0.0008954284,0.0007457354,0.0002999541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003545491,"about_ca_system_score_gemma":0.000353771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003194764,"about_ca_topic_score_gemma":0.0003920506,"domain_scores_codex":[0.9989993,0.0003745173,0.00004804435,0.0001691447,0.0003553842,0.00005368523],"domain_scores_gemma":[0.9987401,0.0005752468,0.0002189857,0.0002303199,0.0001961036,0.00003926991],"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":[0.0001633469,0.0000495618,0.001831153,0.0004709386,0.00009746002,0.0001148333,0.0001200921,0.01570271,0.8133327,0.005593163,0.0008534458,0.1616706],"study_design_scores_gemma":[0.00002498538,0.0003805377,0.006062454,0.00005863287,0.000133505,0.001823586,0.00005686281,0.3745717,0.601581,0.004505242,0.01065351,0.0001479267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01556059,0.000994049,0.9820482,0.0001179154,0.00002685266,0.00003590581,0.0000837921,0.0005396311,0.0005930047],"genre_scores_gemma":[0.4378032,0.0009580645,0.5592279,0.0002329785,0.00008872583,0.0001577854,0.0002604751,0.0002144202,0.001056506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002169987,"threshold_uncertainty_score":0.0114761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379314014657327,"score_gpt":0.3223820486060981,"score_spread":0.3085889084595248,"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."}}