Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation
This work addresses the need for more effective data augmentation in medical image segmentation, particularly for kidney tumor analysis, but it is incremental as it builds on existing reinforcement learning and augmentation techniques.
The authors tackled the problem of random and potentially harmful data augmentation in medical image segmentation by developing an automatic learning-based method using deep reinforcement learning to optimize augmentation sequences, achieving promising results on CT kidney tumor segmentation with improvements measured by Dice ratio.
Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation. However, the data generated by these conventional augmentation methods are random and sometimes harmful to the subsequent segmentation. In this paper, we developed a novel automatic learning-based data augmentation method for medical image segmentation which models the augmentation task as a trial-and-error procedure using deep reinforcement learning (DRL). In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. Specifically, the best sequential combination of different basic operations is automatically learned by directly maximizing the performance improvement (\ie, Dice ratio) on the available validation set. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.