{"id":"W3200918305","doi":"10.1109/ius52206.2021.9593856","title":"Ultrasound Domain Adaptation Using Frequency Domain Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Synthetic data; Frequency domain; Attenuation; Artificial intelligence; Segmentation; Pattern recognition (psychology); Envelope (radar); Sample (material); Algorithm; Computer vision; Radar; Physics; Optics; Telecommunications","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":[0.0005577357,0.0004377279,0.0003543228,0.0006908054,0.0001685314,0.000538883,0.0004107228,0.0007431425,0.002742077],"category_scores_gemma":[0.002565345,0.0002349093,0.0004804948,0.0006311084,0.0003288077,0.0005436492,0.000591039,0.0007622503,0.001163823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477022,"about_ca_system_score_gemma":0.0004649227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438991,"about_ca_topic_score_gemma":0.001162621,"domain_scores_codex":[0.9997757,0.00005787399,0.00001117472,0.00006658861,0.00006710076,0.00002151487],"domain_scores_gemma":[0.9994486,0.0002268048,0.00004253947,0.0001093288,0.0001566216,0.00001605955],"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":[0.0001692158,0.0001426767,0.001280281,0.0001329586,0.0000814018,0.0001375296,0.0001204948,0.30775,0.1003414,0.00571918,0.003506384,0.5806185],"study_design_scores_gemma":[0.000007766789,0.00004003199,0.0009601889,0.000008368042,0.000009622358,0.0001303308,0.00001451697,0.9770455,0.01465899,0.002945079,0.004164536,0.00001503095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02242269,0.000203513,0.9741082,0.0001350956,0.00008109782,0.00005120027,0.00007730921,0.001158825,0.001761991],"genre_scores_gemma":[0.4108743,0.0004633245,0.5821191,0.0002561399,0.0001046987,0.000186207,0.0006316941,0.0003054454,0.005059098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002742077,"threshold_uncertainty_score":0.009173155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232808493294577,"score_gpt":0.2816445125430099,"score_spread":0.2583636632135522,"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."}}