重新审视超声图像的数据增强
Revisiting Data Augmentation for Ultrasound Images
AI总结：
本文通过构建覆盖14项超声图像分类与分割任务的标准化基准，系统评估了多种数据增强技术的有效性，发现自然图像常用的增强方法（如TrivialAugment）在超声图像上同样甚至更有效，并提出了可推广到其他模态的结构化评估方法。
摘要：
数据增强是一种广泛使用且有效的技术，用于提升深度神经网络的泛化性能。然而，在处理医学图像时尽管经常面临数据可用性有限的问题，数据增强却常常未被充分利用。这似乎源于我们对不同增强技术在不同任务和模态上效果的理解存在差距。超声成像正是这一差距尤为突出的模态之一。本研究通过分析不同增强技术在广泛的超声图像分析任务中提升模型性能的有效性来弥补这一差距。为此，我们引入了一个新的标准化基准，包含来自10个不同来源、覆盖11个身体区域的14项超声图像分类和语义分割任务。我们的结果表明，许多常用于自然图像任务的增强方法在超声图像上同样有效，在某些情况下甚至比专门为超声图像开发的增强方法更为有效。我们还表明，使用TrivialAugment的多样化增强（该方法广泛用于自然图像）对超声图像同样有效。此外，我们提出的方法代表了一种结构化的评估框架，可用于评估各种数据增强方法，并可应用于其他场景和模态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
