Data augmentation for medical image analysis

Deep learning methods develop very rapidly and are widely used in computer vision applications as well as for medical image analysis. The deep learning methods provide a significant improvement on medical image analysis tasks by learning a hierarchical representation of different levels directly from data instead of handcrafted features. However, their superior performance highly relies on the number of available training samples. Lack of data either causes the performance to drop or overfitting problems. Unfortunately, it is not always easy to obtain big data for many applications, especially for medical images. In this chapter, we will discuss data augmentation methods including both traditional transformations and emerging generative adversarial networks. In traditional augmentation methods, techniques including geometric transformations and photometric transformations are introduced, e.g., image color space transformation, image rotation, random cropping. The work based on synthesis to augment data is presented followed by the challenges and future directions on data augmentation.