{"id":"W3209702343","doi":"10.1117/12.2611292","title":"3D PET image generation with tumour masks using TGAN","year":2022,"lang":"en","type":"preprint","venue":"Medical Imaging 2022: Image Processing","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Synthetic data; Segmentation; Pattern recognition (psychology); Data set; Feature (linguistics); Generator (circuit theory); Computer vision; Generative model; Image (mathematics); Image segmentation; Set (abstract data type); Dice; Mathematics; Generative grammar; Statistics","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":[0.0005343711,0.0004758682,0.0002464287,0.0003013854,0.0001013258,0.0003970462,0.0006277155,0.0005167779,0.001875128],"category_scores_gemma":[0.001229675,0.0002729498,0.0004902299,0.0002348871,0.0002976265,0.0002881261,0.000470359,0.0004554186,0.0004774159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005378948,"about_ca_system_score_gemma":0.0003277324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00158342,"about_ca_topic_score_gemma":0.001802379,"domain_scores_codex":[0.9998406,0.00003767369,0.000007024596,0.00004157823,0.00005727337,0.00001573959],"domain_scores_gemma":[0.9996641,0.0001629845,0.00003123354,0.00006739762,0.00005619919,0.00001808904],"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":[0.0002556075,0.0001028921,0.001988446,0.0001288944,0.00007135966,0.0003225922,0.0001164064,0.7634312,0.07117481,0.004611852,0.004586857,0.1532091],"study_design_scores_gemma":[0.000009447844,0.00003080814,0.0003211691,0.000003307958,0.000004224966,0.00009264183,0.000005245544,0.9857094,0.01195009,0.0009946959,0.0008720712,0.000006817602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07473093,0.0001615523,0.9164469,0.0002568499,0.00009235527,0.0001636743,0.000386986,0.004108129,0.003652643],"genre_scores_gemma":[0.5870493,0.000122234,0.4080888,0.0002144369,0.0000231585,0.0002099086,0.001086222,0.0005135046,0.002692339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001875128,"threshold_uncertainty_score":0.006272972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597524176139596,"score_gpt":0.3240131242369655,"score_spread":0.3080378824755695,"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."}}