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Conventional data augmentation realized by performing simple pre-processingoperations (\eg, rotation, crop, \etc) has been validated for its advantage inenhancing the performance for medical image segmentation. However, the datagenerated by these conventional augmentation methods are random and sometimesharmful to the subsequent segmentation. In this paper, we developed a novelautomatic learning-based data augmentation method for medical imagesegmentation which models the augmentation task as a trial-and-error procedureusing deep reinforcement learning (DRL). In our method, we innovatively combinethe data augmentation module and the subsequent segmentation module in anend-to-end training manner with a consistent loss. Specifically, the bestsequential combination of different basic operations is automatically learnedby directly maximizing the performance improvement (\ie, Dice ratio) on theavailable validation set. We extensively evaluated our method on CT kidneytumor segmentation which validated the promising results of our method.

Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation