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.
@article{arxiv.1910.08728,
title = {MixModule: Mixed CNN Kernel Module for Medical Image Segmentation},
author = {Henry H. Yu and Xue Feng and Hao Sun and Ziwen Wang},
journal= {arXiv preprint arXiv:1910.08728},
year = {2020}
}