Analyzing Data Augmentation for Medical Images: A Case Study in
  Ultrasound Images
Adam Tupper Christian Gagné
Data augmentation is one of the most effective techniques to improve the
generalization performance of deep neural networks. Yet, despite often facing
limited data availability in medical image analysis, it is frequently
underutilized. This appears to be due to a gap in our collective understanding
of the efficacy of different augmentation techniques across medical imaging
tasks and modalities. One domain where this is especially true is breast
ultrasound images. This work addresses this issue by analyzing the
effectiveness of different augmentation techniques for the classification of
breast lesions in ultrasound images. We assess the generalizability of our
findings across several datasets, demonstrate that certain augmentations are
far more effective than others, and show that their usage leads to significant
performance gains.