{"id":"W2959573983","doi":"10.1109/isbi.2019.8759216","title":"Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Frame rate; Computer science; Imaging phantom; Focus (optics); Frame (networking); Artificial intelligence; Deep learning; Generative adversarial network; Adversarial system; Ultrasound imaging; Computer vision; Generative grammar; Reduction (mathematics); Image resolution; Line (geometry); Image (mathematics); Ultrasound; Optics; Acoustics; Mathematics; 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":[0.0007130557,0.001009321,0.0005880251,0.0003925887,0.0001765661,0.0005055098,0.001034343,0.001011355,0.001309651],"category_scores_gemma":[0.001401012,0.0005400085,0.0007150025,0.0002944121,0.0006331143,0.0006575017,0.001254158,0.001579782,0.0003757177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005669675,"about_ca_system_score_gemma":0.0003747779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001921631,"about_ca_topic_score_gemma":0.002571258,"domain_scores_codex":[0.9997196,0.00008790931,0.000008208701,0.00006635425,0.00008779619,0.0000301554],"domain_scores_gemma":[0.9994509,0.0003303635,0.00007207449,0.00005832714,0.00005603032,0.0000323631],"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":[0.0000738935,0.00003317061,0.0004151385,0.00003884704,0.00005113355,0.0001030917,0.00003749994,0.9345703,0.009479273,0.0041753,0.001154067,0.04986833],"study_design_scores_gemma":[0.000001931978,0.000008925372,0.00004333567,0.000002329584,0.00000299567,0.00002684949,0.000001408836,0.9976616,0.00104208,0.00101763,0.0001877369,0.000003236547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006559079,0.0001745841,0.9917061,0.0001391041,0.00001987039,0.00002083714,0.00003079113,0.0004380751,0.0009115697],"genre_scores_gemma":[0.6541603,0.0005901017,0.3386535,0.000547408,0.00009616833,0.0001472598,0.0002790632,0.0002468367,0.005279269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001921631,"threshold_uncertainty_score":0.004381239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007982868099269683,"score_gpt":0.2125345966787634,"score_spread":0.2045517285794937,"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."}}