MCNMF-Unet: a mixture Conv-MLP network with multi-scale features fusion Unet for medical image segmentation.

Recently, the medical image segmentation scheme combining Vision Transformer (ViT) and multilayer perceptron (MLP) has been widely used. However, one of its disadvantages is that the feature fusion ability of different levels is weak and lacks flexible localization information. To reduce the semantic gap between the encoding and decoding stages, we propose a mixture conv-MLP network with multi-scale features fusion Unet (MCNMF-Unet) for medical image segmentation. MCNMF-Unet is a U-shaped network based on convolution and MLP, which not only inherits the advantages of convolutional in extracting underlying features and visual structures, but also utilizes MLP to fuse local and global information of each layer of the network. MCNMF-Unet performs multi-layer fusion and multi-scale feature map skip connections in each network stage so that all the feature information can be fully utilized and the gradient disappearance problem can be alleviated. Additionally, MCNMF-Unet incorporates a multi-axis and multi-windows MLP module. This module is fully end-to-end and eliminates the need to consider the negative impact of image cropping. It not only fuses information from multiple dimensions and receptive fields but also reduces the number of parameters and computational complexity. We evaluated the proposed model on BUSI, ISIC2018 and CVC-ClinicDB datasets. The experimental results show that the performance of our proposed model is superior to most existing networks, with an IoU of 84.04% and a F1-score of 91.18%.

FM-Unet: Biomedical image segmentation based on feedback mechanism Unet.

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.