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http://dx.doi.org/10.22710/JICT.2021.11.1.001

An Efficient Data Augmentation for 3D Medical Image Segmentation  

Park, Sangkun (Department of Mechanical Engineering, Korea National University of Transportation)
Publication Information
Journal of Institute of Convergence Technology / v.11, no.1, 2021 , pp. 1-5 More about this Journal
Abstract
Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.
Keywords
deep learning; data augmentation; medical image segmentation; deep neural networks;
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