DSML-UNet: Depthwise separable convolution network with multiscale large kernel for medical image segmentation

Computer-aided diagnosis is becoming increasingly important in modern medicine, and computer-aided diagnosis systems require accurate and automatic segmentation of medical images. Due to the unique jump connection structure, U-based convolutional neural networks (CNN) have been widely used in medical image segmentation tasks. However, the CNN-based method has the small receptive field and is not effective in learning relationships between distant features. To address this problem, a depthwise separable convolution network with multiscale large kernel for medical image segmentation (DSML-UNet) is proposed. The depthwise separable property of large kernel depthwise separable convolution increases the receptive field while adding little computational complexity. In the DSML-UNet, different sizes of large kernel depth-separable convolution modules are proposed. The different sizes of large kernel convolution provide multi-scale information and large receptive fields, which can effectively learn the rich semantic information and relationship between distant features. The proposed DSML-UNet is used in the spine dataset (SpineSagT2Wdataset3), the skin dataset (ISIC) and the lung dataset (FML) with Dice Scores of 0.8273, 0.8159, and 0.9581 respectively. The experimental results show that DSML-UNet improves the segmentation performance compared with other related advanced work.