{"id":"W4389883609","doi":"10.32920/24625203","title":"Synthetic Data Generation Through Image Translation for Improving Out-of-domain MRI Lesion Segmentation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Synthetic data; Segmentation; Translation (biology); Scanner; Pattern recognition (psychology); Noise (video); Image segmentation; Image (mathematics); Computer vision","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":[0.002519193,0.001103418,0.0007007525,0.0008831485,0.0003185718,0.001113208,0.001076822,0.001064045,0.001497125],"category_scores_gemma":[0.01019645,0.0004882086,0.0009661792,0.0009974911,0.0007583076,0.001177784,0.001169724,0.001452181,0.001109563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007035975,"about_ca_system_score_gemma":0.0007661482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624643,"about_ca_topic_score_gemma":0.002605543,"domain_scores_codex":[0.999018,0.0004094059,0.00006929675,0.0002457786,0.0001935099,0.00006398935],"domain_scores_gemma":[0.9955189,0.002282883,0.0003496925,0.000945121,0.0007871203,0.0001160871],"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":[0.0009223489,0.0003504299,0.004620461,0.0003956913,0.0001908161,0.0002627703,0.0004668259,0.5526162,0.05099581,0.006459323,0.007420253,0.3752991],"study_design_scores_gemma":[0.00002494954,0.0001142967,0.0007339254,0.00001432582,0.00001800527,0.0001251407,0.00003632048,0.9747398,0.01936386,0.002617832,0.002192975,0.00001853212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1739288,0.0006647038,0.8162018,0.0005592293,0.0002308982,0.0001792871,0.0007907183,0.005281754,0.002162694],"genre_scores_gemma":[0.5757917,0.0003730708,0.4152236,0.0003491387,0.0001002466,0.0002355584,0.00489735,0.0008233904,0.002205971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002624643,"threshold_uncertainty_score":0.01332289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1424671215031968,"score_gpt":0.4002906982812482,"score_spread":0.2578235767780513,"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."}}