Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low computational cost. A model with higher parameters can theoretically achieve better performance but also result in more computational complexity and higher memory usage, and thus is not practical to implement. In this paper, we look for a lightweight U-Net-based model which can remain the same or even achieve better performance, namely U-Lite. We design U-Lite based on the principle of Depthwise Separable Convolution so that the model can both leverage the strength of CNNs and reduce a remarkable number of computing parameters. Specifically, we propose Axial Depthwise Convolutions with kernels 7x7 in both the encoder and decoder to enlarge the model receptive field. To further improve the performance, we use several Axial Dilated Depthwise Convolutions with filters 3x3 for the bottleneck as one of our branches. Overall, U-Lite contains only 878K parameters, 35 times less than the traditional U-Net, and much more times less than other modern Transformer-based models. The proposed model cuts down a large amount of computational complexity while attaining an impressive performance on medical segmentation tasks compared to other state-of-the-art architectures. The code will be available at: https://github.com/duong-db/U-Lite.

翻译：卷积神经网络（CNN）和基于Transformer的模型因其能够提取高层特征并捕捉图像的关键信息，而被广泛应用于医学图像分割。然而，高精度需求与低计算成本之间往往存在权衡。参数较多的模型理论上能实现更优性能，但也会带来更高的计算复杂度和内存占用，因此实际部署并不现实。本文旨在寻找一种轻量级、基于U-Net的模型，使其在保持甚至提升性能的同时降低复杂度，即U-Lite。我们基于深度可分离卷积原理设计U-Lite，使模型既能发挥CNN的优势，又能显著减少计算参数。具体而言，我们在编码器和解码器中均采用7×7核的轴向深度卷积以扩大模型感受野。为进一步提升性能，我们在瓶颈层使用多个3×3滤波器的轴向空洞深度卷积作为分支之一。总体而言，U-Lite仅包含87.8万个参数，是传统U-Net的1/35，且远少于其他现代基于Transformer的模型。与其他最先进架构相比，所提出的模型在显著降低计算复杂度的同时，在医学分割任务上取得了令人瞩目的性能。代码将开源至：https://github.com/duong-db/U-Lite。