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

ABSTRACT: 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

SUBMITTER: Yuan L 

PROVIDER: S-EPMC10803052 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature