MD-UNet: a medical image segmentation network based on mixed depthwise convolution

In the process of cancer diagnosis and treatment, accurate extraction of the lesion area helps the doctor to judge the condition. Currently, medical image segmentation algorithms based on UNet have been verified to be able to play an important role in clinical diagnosis. However, these methods still have the following drawbacks in extracting the region of interest (ROI): (1) ignoring the intra-class variability of medical images. (2) Failure to obtain effective feature redundancy. To address these problems, a U-shaped medical image segmentation network based on a Mixed depthwise convolution residual module (MDRM), called MD-UNet, is proposed in this paper. In MD-UNet, the MDRM built with a Mixed depthwise convolution attention block (MDAB) captures both local and global dependencies in the image to mitigate the effects of intra-class differences. MDAB captures valid redundant features and further captures global features of the input data. At the same time, the lightweight MDAB senses changes in the receptive field and generates multiple feature mappings. Compared with UNeXt on the ISIC2018 dataset, the MD-UNet segmentation accuracy Dice and IoU are improved by 1.33% and 1.91%, respectively. The code is available at https://github.com/Cloud-Liu/MD-UNet.