Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
- URL: http://arxiv.org/abs/2305.15777v2
- Date: Thu, 31 Aug 2023 07:20:34 GMT
- Title: Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
- Authors: Xinyue Xu, Yuhan Hsi, Haonan Wang, Xiaomeng Li
- Abstract summary: We present Dynamic Data Augmentation (DDAug), which is efficient and has negligible cost.
DDAug computation develops a hierarchical tree structure to represent various augmentations.
Our method outperforms the current state-of-the-art data augmentation strategies.
- Score: 19.780410411548935
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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