Data augmentation is essential in medical imaging for improving
classification accuracy, lesion detection, and organ segmentation under limited
data conditions. However, two significant challenges remain. First, a
pronounced domain gap between natural photographs and medical images can
distort critical disease features. Second, augmentation studies in medical
imaging are fragmented and limited to single tasks or architectures, leaving
the benefits of advanced mix-based strategies unclear. To address these
challenges, we propose a unified evaluation framework with six mix-based
augmentation methods integrated with both convolutional and transformer
backbones on brain tumour MRI and eye disease fundus datasets. Our
contributions are threefold. (1) We introduce MediAug, a comprehensive and
reproducible benchmark for advanced data augmentation in medical imaging. (2)
We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and SnapMix
with ResNet-50 and ViT-B backbones. (3) We demonstrate through extensive
experiments that MixUp yields the greatest improvement on the brain tumor
classification task for ResNet-50 with 79.19% accuracy and SnapMix yields the
greatest improvement for ViT-B with 99.44% accuracy, and that YOCO yields the
greatest improvement on the eye disease classification task for ResNet-50 with
91.60% accuracy and CutMix yields the greatest improvement for ViT-B with
97.94% accuracy. Code will be available at
https://github.com/AIGeeksGroup/MediAug.