  Deep neural networks have been widely used in medical image analysis and
medical image segmentation is one of the most important tasks. U-shaped neural
networks with encoder-decoder are prevailing and have succeeded greatly in
various segmentation tasks. While CNNs treat an image as a grid of pixels in
Euclidean space and Transformers recognize an image as a sequence of patches,
graph-based representation is more generalized and can construct connections
for each part of an image. In this paper, we propose a novel ViG-UNet, a graph
neural network-based U-shaped architecture with the encoder, the decoder, the
bottleneck, and skip connections. The downsampling and upsampling modules are
also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and
Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most
existing classic and state-of-the-art U-shaped networks.
