{"id":"W2988197594","doi":"10.1109/embc.2019.8857242","title":"Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Principal component analysis; GLUE; Elastography; Computer science; Displacement (psychology); Displacement field; Feature (linguistics); Algorithm; Artificial intelligence; Pattern recognition (psychology); Mathematics; Ultrasound; Acoustics; Physics; Finite element method; Engineering","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":[0.00119294,0.001365578,0.001009145,0.001225712,0.0004327002,0.001248947,0.0008725832,0.001099311,0.002606862],"category_scores_gemma":[0.005948035,0.0008976964,0.0009168538,0.001532842,0.0006650799,0.001717519,0.001229843,0.001908986,0.001651989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004435279,"about_ca_system_score_gemma":0.00102807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003055569,"about_ca_topic_score_gemma":0.004170015,"domain_scores_codex":[0.9992367,0.0002062522,0.00004276207,0.0001701937,0.0002980448,0.0000460791],"domain_scores_gemma":[0.9981608,0.001088028,0.000152114,0.0002493592,0.000294697,0.0000549839],"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":[0.0002452667,0.00008112432,0.001269313,0.0003328484,0.0001462208,0.0001651305,0.0002035379,0.3047892,0.04520435,0.01129903,0.004537531,0.6317264],"study_design_scores_gemma":[0.00001512601,0.00005035233,0.0006458077,0.00001982448,0.00001786357,0.0001905383,0.0000307006,0.9761604,0.01092001,0.008357802,0.003562464,0.00002907729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002586973,0.0001578912,0.9965047,0.00005195132,0.00001668967,0.00001962003,0.00003605691,0.0004593912,0.0001666577],"genre_scores_gemma":[0.07057938,0.0006373815,0.9260622,0.00006169844,0.00005295426,0.0001273803,0.0003157926,0.0003678414,0.001795305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003055569,"threshold_uncertainty_score":0.008720815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439701382496845,"score_gpt":0.2745512831171075,"score_spread":0.260154269292139,"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."}}