Test-time generative augmentation for medical image segmentation
测试时生成增强用于医学图像分割
机构 * organization= School of Computer Science ； Engineering, Nanjing University of Science ； organization= Bioengineering Department ； Imperial-X, Imperial College London , city= London , postcode= W12 7SL , country= UK ； organization= Digital Medical Research Center, School of Basic Medical Sciences, Fudan University , city= Shanghai , country= China ； organization= Shanghai Key Laboratory of MICCAI , city= Shanghai , country= China ； organization= School of Biomedical Engineering, Shenzhen University , city= Shenzhen , country= China ； organization= Department of Computer Science ； Engineering, The Hong Kong University of Science ； Engineering, The Chinese University of Hong Kong , city= Hong Kong , country= China ； Lung Institute, Imperial College London , city= London , postcode= SW7 2AZ , country= UK ； organization= Cardiovascular Research Centre, Royal Brompton Hospital , city= London , postcode= SW3 6NP , country= UK ； organization= School of Biomedical Engineering \& Imaging Sciences, King's College London , city= London , postcode= WC2R 2LS , country= UK
AI总结 本研究提出TTGA方法，通过生成模型在测试时增强医学图像分割，提升分割精度并提供像素级误差估计。
Comments Accepted for publication in Medical Image Analysis (MedIA). Finalized version. Vol. 109, March 2026
Journal ref Medical Image Analysis, Vol. 109, 103902, 2026
