Models based on U-like structures have improved the performance of medical
image segmentation. However, the single-layer decoder structure of U-Net is too
"thin" to exploit enough information, resulting in large semantic differences
between the encoder and decoder parts. Things get worse if the number of
training sets of data is not sufficiently large, which is common in medical
image processing tasks where annotated data are more difficult to obtain than
other tasks. Based on this observation, we propose a novel U-Net model named
MS-UNet for the medical image segmentation task in this study. Instead of the
single-layer U-Net decoder structure used in Swin-UNet and TransUnet, we
specifically design a multi-scale nested decoder based on the Swin Transformer
for U-Net. The proposed multi-scale nested decoder structure allows the feature
mapping between the decoder and encoder to be semantically closer, thus
enabling the network to learn more detailed features. In addition, we propose a
novel edge loss and a plug-and-play fine-tuning Denoising module, which not
only effectively improves the segmentation performance of MS-UNet, but could
also be applied to other models individually. Experimental results show that
MS-UNet could effectively improve the network performance with more efficient
feature learning capability and exhibit more advanced performance, especially
in the extreme case with a small amount of training data, and the proposed Edge
loss and Denoising module could significantly enhance the segmentation
performance of MS-UNet.

基于医学图像分割任务，我们提出了一种名为 MS-UNet 的新型 U-Net 模型。与 Swin-UNet 和 TransUnet 中使用的单层 U-Net 解码器结构不同，我们专门为 U-Net 设计了基于 Swin Transformer 的多尺度嵌套解码器。我们还提出了一种新颖的边界损失和即插即用微调去噪模块，这不仅有效提高了 MS-UNet 的分割性能，还可以单独应用于其他模型。实验证明，MS-UNet 在更高效的特征学习能力方面有效提升了网络性能，尤其是在少量训练数据的极端情况下，并且所提出的边界损失和去噪模块能显著增强 MS-UNet 的分割性能。