Revisiting Data Augmentation for Ultrasound Images

2025-01-22

AI Summary

Medical ultrasound image augmentation lacks standardized evaluation protocols, hindering fair comparison and adoption of data augmentation techniques in deep learning. Method: We introduce the first standardized multi-task benchmark for ultrasound imaging, encompassing 10 diverse data sources, 11 anatomical regions, and 14 classification and segmentation tasks, coupled with a transferable, structured augmentation evaluation framework. Contribution/Results: Extensive experiments reveal that general-purpose augmentation methods designed for natural images—particularly TrivialAugment—significantly outperform conventional ultrasound-specific augmentations, yielding average accuracy improvements of 3.2–7.8% across tasks. This work is the first to empirically demonstrate the superiority of generic augmentation strategies in ultrasound analysis, challenging domain-specific assumptions. It provides a plug-and-play, cross-task generalizable augmentation guideline for low-data medical imaging scenarios, enabling robust model training without task- or modality-specific tuning.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Data Augmentation
• Ultrasound Imaging
• Deep Learning

Innovation

• Data Augmentation
• Ultrasound Imaging
• TrivialAugment