High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

ABSTRACT: The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuou

SUBMITTER: Wei C 

PROVIDER: S-EPMC10099222 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature