DPNet: A Dual-Path Network With Distance-Aware Attention for Medical Image Segmentation

Accurate and automated segmentation of medical images is crucial for enhancing the efficiency of disease diagnosis and treatment. In the past few years, there has been a proliferation of models based on convolutional neural networks (CNN), and they have achieved remarkable results in various medical image segmentation tasks. However, due to the limitations of the convolutional receptive field, it is impossible to effectively extract long-range contextual features. Although the receptive field theoretically increases gradually with the deepening of the neural network hierarchy, it still restricts the network’s ability to learn the long-range dependencies during the encoding process. To address these challenges, this paper proposes a dual-path network with Distance-Aware Attention for medical image segmentation (DPNet). Specifically, we open up an auxiliary path in the model encoder’s deep layer to learn the image’s global contextual information. The module for learning global feature information is called Distance-Aware Attention (DAA) Module, which determines the degree of attention between different parts of the image based on the distance between them, so that the attention is more targeted. In addition, we design a Channel-Spatial Attention Guidance (CSAG) module to let the extracted global features guide the local features to fully utilize the extracted global information and achieve the segmentation effect at a higher level. In order to verify the rationality of our model design, we conduct extensive experiments on the datasets ISIC2017 and ISIC2018, and the experimental results show that DPNet outperforms other state-of-the-art segmentation models. The code is available at https://github.com/w573352/DPNet.