A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

Journal: PloS one
PMID: 34081736

Abstract

PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images.

Authors

  • Fan Xu
    Department of Public Health, Chengdu Medical College, Sichuan, China.
  • Yikun Qin
    China-ASEAN Information Harbor, Nanning, Guangxi, China.
  • Wenjing He
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Guangyi Huang
    Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Jian Lv
    Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
  • Xinxin Xie
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Chunli Diao
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Fen Tang
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Li Jiang
    School of Food Science and Engineering, Hefei University of Technology, Hefei, China.
  • Rushi Lan
    Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, Guangxi, China.
  • Xiaohui Cheng
    Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, Guangxi, China.
  • Xiaolin Xiao
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
  • Siming Zeng
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Ling Cui
    Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Ningning Tang
    Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.

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