MediAug: Exploring Visual Augmentation in Medical Imaging
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.
681470d72175e5e7ca0ea002
Xuyin Qi
admin_assigned
2025-05-05T07:49:14.835Z
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/avatars/29eaf9a849cb6b461f44eee7b8f0c482.svg
Xuyin Qi
user
JustDelet
681470d72175e5e7ca0ea003
Zeyu Zhang
claimed_verified
2025-05-04T10:05:01.612Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
681470d72175e5e7ca0ea004
Canxuan Gang
admin_assigned
2025-05-05T07:49:21.073Z
67e3f7c87b1778009c4fe2d7
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/p-5bJK_CU3TD3ieRLDebp.png
Canxuan Gang
user
Canxuan
681470d72175e5e7ca0ea005
Hao Zhang
681470d72175e5e7ca0ea006
Lei Zhang
681470d72175e5e7ca0ea007
Zhiwei Zhang
681470d72175e5e7ca0ea008
Yang Zhao
MediAug: Exploring Visual Augmentation in Medical Imaging
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.
A unified evaluation framework integrates mix-based augmentation methods with convolutional and transformer backbones to improve medical imaging tasks.
MediAug MixUp YOCO CropMix CutMix AugMix SnapMix ResNet-50 ViT-B brain tumor classification eye disease classification