Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
This addresses the challenge of manual augmentation configuration for medical imaging, which is incremental as it builds on automatic data augmentation methods but focuses on efficiency and dataset-specific optimization.
The paper tackles the problem of overfitting in deep learning models for medical image segmentation due to limited data by proposing Dynamic Data Augmentation (DDAug), which uses a hierarchical tree and Monte-Carlo tree search to automatically optimize augmentation pipelines for each dataset, resulting in outperforming state-of-the-art strategies on multiple Prostate MRI datasets.
Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions. Therefore, automatic data augmentation is proposed to learn favorable augmentation strategies for different datasets while incurring large GPU overhead. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. Our DDAug develops a hierarchical tree structure to represent various augmentations and utilizes an efficient Monte-Carlo tree searching algorithm to update, prune, and sample the tree. As a result, the augmentation pipeline can be optimized for each dataset automatically. Experiments on multiple Prostate MRI datasets show that our method outperforms the current state-of-the-art data augmentation strategies.