LightAWNet: Lightweight adaptive weighting network based on dynamic convolutions for medical image segmentation.

Journal: Journal of applied clinical medical physics
Published Date: Dec 1, 2024

Abstract

PURPOSE: The complexity of convolutional neural networks (CNNs) can lead to improved segmentation accuracy in medical image analysis but also results in increased network complexity and training challenges, especially under resource limitations. Conversely, lightweight models offer efficiency but often sacrifice accuracy. This paper addresses the challenge of balancing efficiency and accuracy by proposing LightAWNet, a lightweight adaptive weighting neural network for medical image segmentation.

Authors

  • Xiaoyan Wang
    Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
  • Jianhao Yu
    School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
  • Bangze Zhang
    School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, People's Republic of China.
  • Xiaojie Huang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, 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.
  • Ming Xia
    Department of Neurosurgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.

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