系统评估数据增强对超声图像的提升效果，发现通用方法比专用方法更有效。
Revisiting Data Augmentation for Ultrasound Images
- 构建14个超声任务的标准基准，覆盖10个数据源和11个身体部位。
- 通用增强如TrivialAugment在超声图像上表现优于部分专用增强方法。
- 为医学影像提供可复用的增强评估框架，适合医疗AI研究者参考。
数据增强是提升深度神经网络泛化能力的有效手段，但在医学图像尤其是超声成像中常因数据有限而未被充分使用。这源于对不同任务与模态下增强技术效能理解不足。本文通过分析多种增强技术在广泛超声图像分析任务中的效果，填补这一空白。为此，我们引入了一个包含14个分类与语义分割任务的新标准化基准，数据来自10个来源，覆盖11个身体部位。结果表明，许多自然图像中常用的数据增强技术在超声图像上同样有效，甚至在某些情况下优于专为超声设计的方法。此外，广泛使用的TrivialAugment在超声图像上也表现出色。本研究提出的评估方法可推广至其他医学影像模态，具有通用性。
原文摘要 · Abstract (English)
Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
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