The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China.
College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China.
Math Biosci Eng. 2023 May 15;20(7):12039-12055. doi: 10.3934/mbe.2023535.
With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
随着深度学习的发展，医学图像分割技术在计算机视觉领域取得了重大进展。Unet 是开创性的工作，许多研究人员在此架构的基础上进行了进一步的研究。然而，我们发现这些架构中的大多数都是对网络的反向传播和集成的改进，很少对网络的正向传播和信息集成进行更改。因此，我们提出了一种反馈机制 Unet（FM-Unet）模型，该模型分别在网络的编码器和解码器路径上添加了反馈路径，以帮助网络融合当前编码器和解码器中下一步的信息。可以很好地解决编码器信息丢失和解码器信息不足的问题。所提出的模型具有更适中的网络参数，并且同时进行多节点信息融合可以缓解梯度消失。我们在两个公共数据集上进行了实验，结果表明 FM-Unet 取得了令人满意的结果。
