MediAug：探索醫學影像中的視覺增強技術

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

在醫學影像領域，數據增強對於提升分類準確性、病變檢測及器官分割在數據有限條件下的表現至關重要。然而，仍存在兩大挑戰。首先，自然照片與醫學影像之間顯著的領域差異可能扭曲關鍵疾病特徵。其次，醫學影像中的增強研究零散且多局限於單一任務或架構，使得先進的混合策略的優勢尚不明確。為應對這些挑戰，我們提出了一個統一的評估框架，整合了六種基於混合的增強方法，並結合卷積和Transformer骨幹網絡，應用於腦腫瘤MRI和眼病眼底數據集。我們的主要貢獻有三點：(1) 我們引入了MediAug，一個全面且可重現的醫學影像高級數據增強基準。(2) 我們系統地評估了MixUp、YOCO、CropMix、CutMix、AugMix和SnapMix與ResNet-50及ViT-B骨幹的結合效果。(3) 通過大量實驗，我們證實MixUp在ResNet-50上對腦腫瘤分類任務提升最大，達到79.19%的準確率，而SnapMix在ViT-B上表現最佳，準確率達99.44%；同時，YOCO在ResNet-50上對眼病分類任務提升最顯著，準確率為91.60%，CutMix則在ViT-B上取得97.94%的準確率，表現最優。代碼將公開於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.