Dual-branch Transformer for semi-supervised medical image segmentation.

Journal: Journal of applied clinical medical physics
Published Date: Aug 12, 2024

Abstract

PURPOSE: In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time-consuming and labor-intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture.

Authors

  • Xiaojie Huang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Yating Zhu
    Zhejiang University of Technology, Hangzhou, China.
  • Minghan Shao
    Zhejiang University of Technology, Hangzhou, China.
  • Ming Xia
    Department of Neurosurgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Xiaoting Shen
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
  • Pingli Wang
    The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Xiaoyan Wang
    Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.

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