MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
This work addresses a specific bottleneck in medical image segmentation for researchers and practitioners, but it is incremental as it builds on existing U-Net architectures.
The paper tackles the problem of fixed convolution kernel sizes in medical image segmentation by proposing a module that combines multiple kernel sizes, applied to U-Net and its variants, resulting in significant improvement on three benchmark datasets.
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.