{"id":"W4379202866","doi":"10.1016/j.compmedimag.2023.102249","title":"DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data","year":2023,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Windsor","funders":"","keywords":"Computer science; Image synthesis; Artificial intelligence; Similarity (geometry); Image (mathematics); Source code; Contrast (vision); Modality (human–computer interaction); Computer vision; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0009290948,0.0002469753,0.000365294,0.0002932227,0.0003522137,0.000369171,0.001675801,0.00005593827,0.00003840926],"category_scores_gemma":[0.0004828472,0.0002225074,0.0000793619,0.001267879,0.0002044832,0.0004974014,0.001971674,0.0002612245,0.00006328862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000127515,"about_ca_system_score_gemma":0.0001208314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002662694,"about_ca_topic_score_gemma":0.00002080832,"domain_scores_codex":[0.9973318,0.0002032517,0.0003393435,0.0009696452,0.0006944968,0.0004614933],"domain_scores_gemma":[0.997261,0.001032714,0.00007105332,0.00099686,0.0000785658,0.0005597987],"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":[0.00002229248,0.0001095733,0.002226079,0.00002420219,0.0001910975,0.0005164879,0.00031366,0.000136153,0.001356882,0.003013296,0.1230749,0.8690154],"study_design_scores_gemma":[0.0003682079,0.00001081539,0.006379595,0.000126744,0.00002250827,0.00003311745,0.00001899923,0.9263898,0.0003938479,0.00163132,0.06432834,0.0002967532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01513597,0.0003933526,0.9561632,0.02544509,0.001826614,0.0001415407,0.00007378172,0.0007235371,0.0000968606],"genre_scores_gemma":[0.828833,0.001291178,0.1586444,0.008706159,0.002014142,0.00004945174,0.0002624791,0.00006022298,0.0001389163],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9262536,"threshold_uncertainty_score":0.907359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03202701701348586,"score_gpt":0.2732771936232786,"score_spread":0.2412501766097927,"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."}}