TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation

Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years, convolutional encoder-decoder solutions have achieved substantial progress in the field of automatic medical image segmentation. Due to the inherent bias in the convolution operations, prior models mainly focus on local visual cues formed by the neighboring pixels, but fail to fully model the long-range contextual dependencies. In this article, we propose a novel Transformer-based Attention Guided Network called TransAttUnet , in which the multi-level guided attention and multi-scale skip connection are designed to jointly enhance the performance of the semantical…

• NN
  National Natural Science Foundation of China

  Awards: 62302172, 62176077
• SF
  Shenzhen Fundamental Research and Discipline Layout project

  Award: JCYJ20210324132210025
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023A1515010057