{"id":"W4402547026","doi":"10.2196/58911","title":"Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss","year":2024,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; National Institutes of Health","keywords":"Preprint; Perception; Image (mathematics); Artificial intelligence; Quality (philosophy); Image quality; Computer science; Pattern recognition (psychology); Computer vision; Psychology; Physics; Neuroscience; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002400776,0.0009956135,0.0005775404,0.0007218021,0.0002044822,0.0006838921,0.001030288,0.0009516208,0.0009067213],"category_scores_gemma":[0.005491709,0.0003416095,0.0007063194,0.0002989655,0.0009750962,0.0007531687,0.001414843,0.001295613,0.0002203749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009914587,"about_ca_system_score_gemma":0.0005759699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488837,"about_ca_topic_score_gemma":0.002076144,"domain_scores_codex":[0.9993857,0.0002034658,0.00002306855,0.0001386683,0.0001825334,0.00006652551],"domain_scores_gemma":[0.9979754,0.001228935,0.0002550209,0.0001855925,0.00027042,0.00008457083],"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":[0.0001766654,0.00007600547,0.002247871,0.00007887852,0.00008705416,0.0001798309,0.00006278988,0.9203754,0.008498967,0.002100859,0.0009144425,0.06520111],"study_design_scores_gemma":[0.000003716739,0.00006354148,0.0004726165,0.000008371146,0.00001557975,0.00007243238,0.000005351083,0.9958989,0.002160077,0.001081966,0.0002103634,0.00000711545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08137743,0.001054511,0.9133614,0.0007828551,0.0001133832,0.0001139398,0.0001091359,0.0005888009,0.002498591],"genre_scores_gemma":[0.9294973,0.000512451,0.06687944,0.0004553198,0.00007348266,0.00008225501,0.0001763802,0.000111932,0.002211484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002488837,"threshold_uncertainty_score":0.01269662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005749693740561004,"score_gpt":0.2833996621228106,"score_spread":0.2776499683822496,"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."}}