{"id":"W3082132458","doi":"10.1109/embc44109.2020.9176101","title":"High Frequency Ultrasound Image Recovery Using Tight Frame Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Ultrasound; High frequency ultrasound; Generative adversarial network; Ultrasound imaging; Computer science; Frame rate; Center frequency; Image resolution; Artificial intelligence; Acoustics; Frame (networking); Adversarial system; Iterative reconstruction; Image (mathematics); Generative grammar; Computer vision; Optics; 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":[0.0009101232,0.001022923,0.0006514002,0.0003836513,0.0002239004,0.0005150501,0.001010102,0.001035496,0.001207425],"category_scores_gemma":[0.002354011,0.0004297557,0.0006546495,0.0002846322,0.0008058597,0.000742262,0.001262582,0.001455258,0.0003795973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579532,"about_ca_system_score_gemma":0.0003953273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925649,"about_ca_topic_score_gemma":0.001629443,"domain_scores_codex":[0.9996215,0.0001204662,0.00001160828,0.0000946993,0.0001057955,0.00004588552],"domain_scores_gemma":[0.9990914,0.0005777068,0.0001138005,0.00009259472,0.00008678293,0.0000377014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001094531,0.00003770266,0.0003752309,0.00003892774,0.00003843451,0.0001423335,0.0000504717,0.9347879,0.008462635,0.005346803,0.0009963131,0.04961381],"study_design_scores_gemma":[0.000002471373,0.00001327129,0.00004989994,0.000002635881,0.000003253149,0.00002755624,0.000002211377,0.9972166,0.001068407,0.001469871,0.0001401573,0.000003619987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01049097,0.0001564782,0.9878619,0.0001438894,0.00002182782,0.00001909018,0.00002353894,0.000358262,0.0009241],"genre_scores_gemma":[0.795616,0.000419385,0.1982414,0.000428077,0.00008727561,0.0001205502,0.0002148691,0.0001952901,0.004677278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001925649,"threshold_uncertainty_score":0.004813194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009866688393849994,"score_gpt":0.197244503622021,"score_spread":0.187377815228171,"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."}}