  UNet and its latest extensions like TransUNet have been the leading medical
image segmentation methods in recent years. However, these networks cannot be
effectively adopted for rapid image segmentation in point-of-care applications
as they are parameter-heavy, computationally complex and slow to use. To this
end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP)
based network for image segmentation. We design UNeXt in an effective way with
an early convolutional stage and a MLP stage in the latent stage. We propose a
tokenized MLP block where we efficiently tokenize and project the convolutional
features and use MLPs to model the representation. To further boost the
performance, we propose shifting the channels of the inputs while feeding in to
MLPs so as to focus on learning local dependencies. Using tokenized MLPs in
latent space reduces the number of parameters and computational complexity
while being able to result in a better representation to help segmentation. The
network also consists of skip connections between various levels of encoder and
decoder. We test UNeXt on multiple medical image segmentation datasets and show
that we reduce the number of parameters by 72x, decrease the computational
complexity by 68x, and improve the inference speed by 10x while also obtaining
better segmentation performance over the state-of-the-art medical image
segmentation architectures. Code is available at
https://github.com/jeya-maria-jose/UNeXt-pytorch
