  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.
