{"id":"W3025060801","doi":"10.1109/tuffc.2020.2994028","title":"Fast Strain Estimation and Frame Selection in Ultrasound Elastography Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Elastography; Displacement (psychology); Artificial intelligence; Computer science; Position (finance); Principal component analysis; Deformation (meteorology); Radio frequency; Computer vision; Acoustics; Ultrasound; Algorithm; Mathematics; Physics; Telecommunications","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":[0.002106299,0.001153491,0.001458982,0.001748991,0.0005815349,0.001042271,0.001517246,0.001654969,0.003472604],"category_scores_gemma":[0.006562726,0.0008644348,0.0008524014,0.001421968,0.0005999549,0.001560505,0.001450483,0.001785381,0.001760619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006976251,"about_ca_system_score_gemma":0.0009813472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006273332,"about_ca_topic_score_gemma":0.006540988,"domain_scores_codex":[0.998893,0.0003447907,0.00006322195,0.0002366004,0.0003321761,0.0001302207],"domain_scores_gemma":[0.9980668,0.001191569,0.000127013,0.0002188788,0.0003061984,0.00008940539],"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":[0.0004772992,0.0001338576,0.001293732,0.0001125146,0.00006998626,0.0001890435,0.0001400735,0.1873551,0.01914319,0.006757619,0.004246396,0.7800812],"study_design_scores_gemma":[0.00001310784,0.00002665515,0.0003565643,0.000008033695,0.000006398506,0.00003103124,0.00001194525,0.9921592,0.00362554,0.002824934,0.0009252835,0.00001142229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009062937,0.0003467294,0.9885786,0.0001318752,0.0000554152,0.0000369978,0.00006391452,0.00129654,0.0004270363],"genre_scores_gemma":[0.1321194,0.0005421891,0.8631439,0.0001351801,0.0001334225,0.0001930096,0.0005762334,0.0003394443,0.002817268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006273332,"threshold_uncertainty_score":0.01247364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198495540509968,"score_gpt":0.2351009974913251,"score_spread":0.2231160420862254,"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."}}