MSDANet: A multi-scale dilation attention network for medical image segmentation

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder-decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods.