title: MediAug: Exploring Visual Augmentation in Medical Imaging
summary: 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.
paper_authors._id: 681470d72175e5e7ca0ea002
paper_authors.name: Xuyin Qi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:49:14.835Z
paper_authors._id: 648d3ec351ab9e87e18edbe1
paper_authors.avatarUrl: /avatars/29eaf9a849cb6b461f44eee7b8f0c482.svg
paper_authors.fullname: Xuyin Qi
paper_authors.type: user
paper_authors.user: JustDelet
paper_authors._id: 681470d72175e5e7ca0ea003
paper_authors.name: Zeyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:05:01.612Z
paper_authors._id: 64ec877bb93654d4ca5c92e9
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paper_authors.fullname: Zeyu Zhang
paper_authors.type: user
paper_authors.user: SteveZeyuZhang
paper_authors._id: 681470d72175e5e7ca0ea004
paper_authors.name: Canxuan Gang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:49:21.073Z
paper_authors._id: 67e3f7c87b1778009c4fe2d7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/p-5bJK_CU3TD3ieRLDebp.png
paper_authors.fullname: Canxuan Gang
paper_authors.type: user
paper_authors.user: Canxuan
paper_authors._id: 681470d72175e5e7ca0ea005
paper_authors.name: Hao Zhang
paper_authors._id: 681470d72175e5e7ca0ea006
paper_authors.name: Lei Zhang
paper_authors._id: 681470d72175e5e7ca0ea007
paper_authors.name: Zhiwei Zhang
paper_authors._id: 681470d72175e5e7ca0ea008
paper_authors.name: Yang Zhao
paper_title: MediAug: Exploring Visual Augmentation in Medical Imaging
paper_summary: 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.
paper_ai_summary: A unified evaluation framework integrates mix-based augmentation methods with convolutional and transformer backbones to improve medical imaging tasks.
paper_ai_keywords: MediAug MixUp YOCO CropMix CutMix AugMix SnapMix ResNet-50 ViT-B brain tumor classification eye disease classification