MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 9, 2024

Abstract

BACKGROUND AND OBJECTIVE: Medical image segmentation has been significantly improved in recent years with the progress of Convolutional Neural Networks (CNNs). Due to the inherent limitations of convolutional operations, CNNs perform poorly in learning the correlation information between global and long-range features. To solve this problem, some existing solutions rely on building deep encoders and down-sampling operations, but such methods are prone to produce redundant network structures and lose local details. Therefore, medical image segmentation tasks require better solutions to improve the modeling of the global context, while maintaining a strong grasp of the low-level details.

Authors

  • Chuan Wang
    Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
  • Mingfeng Jiang
    School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Bo Wei
    1 Department of General Surgery, Chinese PLA General Hospital, Beijing 100853, China.
  • Yongming Li
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou, China.
  • Pin Wang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.

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