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arXiv · 1910.08728

MixModule: Mixed CNN Kernel Module for Medical Image Segmentation

Henry H. Yu·Xue Feng·Hao Sun·Ziwen Wang
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Abstract

Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and we apply the proposed module to U-Net and its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.

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Henry H. Yu, Xue Feng, Hao Sun, Ziwen Wang. 2019-10-19. MixModule: Mixed CNN Kernel Module for Medical Image Segmentation. https://arxiv.org/abs/1910.08728

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