Effective high-to-low-level feature aggregation network for endoscopic image classification.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 14, 2022

Abstract

PURPOSE: The accuracy improvement in endoscopic image classification matters to the endoscopists in diagnosing and choosing suitable treatment for patients. Existing CNN-based methods for endoscopic image classification tend to use the deepest abstract features without considering the contribution of low-level features, while the latter is of great significance in the actual diagnosis of intestinal diseases.

Authors

  • Sheng Li
    School of Data Science, University of Virginia, Charlottesville, VA, United States.
  • Jiafeng Yao
  • Jing Cao
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Xueting Kong
    College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China.
  • Jinhui Zhu
    The Second Affiliated Hospital of Hospital of Zhejiang University School of Medicine, Hangzhou, 310023, Zhejiang, People's Republic of China. 2512016@zju.edu.cn.

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