ConvUNeXt: An efficient convolution neural network for medical image segmentation

Recently, ConvNeXts constructing from standard ConvNet modules has produced competitive performance in various image applications. In this paper, an efficient model based on the classical UNet, which can achieve promising results with a low number of parameters, is proposed for medical image segmentation. Inspired by ConvNeXt, the designed model is called ConvUNeXt and towards reduction in the amount of parameters while retaining outstanding segmentation superiority. Specifically, we firstly improved the convolution block of UNet by using large convolution kernels and depth-wise separable convolution to considerably decrease the number of parameters; then residual connections in both encoder and decoder are added and pooling is abandoned via adopting convolution for down-sampling; during skip connection, a lightweight attention mechanism is designed to filter out noise in low-level semantic information and suppress irrelevant features, so that the network can pay more attention to the target area. Compared to the standard UNet, our model has 20% fewer parameters, meanwhile, experimental results on different datasets show that it exhibits superior segmentation performance when the amount of data is scarce or sufficient. Code will be available at https://github.com/1914669687/ConvUNeXt. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an efficient model called ConvUNeXt, based on the classical UNet, for medical image segmentation with a low number of parameters. The model incorporates improvements such as larger convolution kernels, depth-wise separable convolution, residual connections, and a lightweight attention mechanism to enhance segmentation performance. Experimental results demonstrate superior performance compared to the standard UNet, particularly with limited data.