{"id":"W3041500444","doi":"10.1016/j.media.2020.101770","title":"Supervised learning with cyclegan for low-dose FDG PET image denoising","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":194,"is_retracted":false,"has_abstract":false,"ca_institutions":"Siemens (Canada); University Health Network","funders":"National University Cancer Institute, Singapore","keywords":"Artificial intelligence; Noise reduction; Image denoising; Computer science; Pattern recognition (psychology); Computer vision; Supervised learning; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005159642,0.0002390979,0.000726627,0.0001830663,0.000195656,0.00008476505,0.0002833427,0.00009045738,0.002100034],"category_scores_gemma":[0.001922644,0.0001780772,0.0003977794,0.001483833,0.0003423656,0.0001393229,0.00009492382,0.0006290942,0.00006400408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003944257,"about_ca_system_score_gemma":0.0001744472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007507787,"about_ca_topic_score_gemma":0.000009929992,"domain_scores_codex":[0.9975172,0.00006991743,0.0004752213,0.0005868674,0.0009062563,0.0004445893],"domain_scores_gemma":[0.9978591,0.0002212395,0.0001137101,0.0003958447,0.0002604372,0.001149729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002480651,0.003467251,0.05389986,0.00358705,0.0128043,0.008671779,0.004384463,0.0001075537,0.6220152,0.001135281,0.1598252,0.1276214],"study_design_scores_gemma":[0.005659722,0.0008308559,0.002989137,0.0003861604,0.01012502,0.0001682773,0.0008695956,0.9282851,0.01845267,0.0001233587,0.03131316,0.0007969437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966354,0.00005161424,0.7156268,0.08556194,0.00001090932,0.0005432566,0.00001305153,0.0005097593,0.001047271],"genre_scores_gemma":[0.8072988,0.00007347823,0.1822937,0.00886226,0.0004177228,0.0001516965,0.0004081685,0.00006459629,0.000429582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9281775,"threshold_uncertainty_score":0.9988122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569687144382458,"score_gpt":0.3122459519873317,"score_spread":0.2965490805435071,"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."}}