MediAug：探索医学影像中的视觉增强技术

MediAug: Exploring Visual Augmentation in Medical Imaging

April 26, 2025

作者: Xuyin Qi, Zeyu Zhang, Canxuan Gang, Hao Zhang, Lei Zhang, Zhiwei Zhang, Yang Zhao

cs.AI

摘要

在医学影像领域，数据增强对于在有限数据条件下提升分类精度、病灶检测及器官分割至关重要。然而，仍存在两大挑战。首先，自然照片与医学图像间显著的领域差异可能扭曲关键疾病特征。其次，医学影像中的增强研究零散且局限于单一任务或架构，使得先进混合策略的益处尚不明确。为应对这些挑战，我们提出了一种统一评估框架，整合了六种基于混合的增强方法，并应用于脑肿瘤MRI和眼病眼底数据集，同时结合了卷积与Transformer骨干网络。我们的贡献有三方面：(1) 引入MediAug，为医学影像中的高级数据增强提供了一个全面且可复现的基准。(2) 系统评估了MixUp、YOCO、CropMix、CutMix、AugMix和SnapMix与ResNet-50及ViT-B骨干网络的结合效果。(3) 通过大量实验证明，MixUp在ResNet-50上对脑肿瘤分类任务提升最大，准确率达79.19%，而SnapMix在ViT-B上表现最佳，准确率达99.44%；YOCO在ResNet-50上对眼病分类任务提升最为显著，准确率为91.60%，CutMix则在ViT-B上取得最大提升，准确率为97.94%。代码将发布于https://github.com/AIGeeksGroup/MediAug。

English

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.