Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

Journal: Biomedical engineering online
Published Date: Nov 21, 2017

Abstract

BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial.

Authors

  • Jiewei Jiang
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China.
  • Xiyang Liu
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China. xyliu@xidian.edu.cn.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Erping Long
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Liming Wang
    School of Information and Communication Engineering, North University of China, Taiyuan 030051, China. wlm@nuc.edu.cn.
  • Wangting Li
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Lin Liu
    Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
  • Shuai Wang
    Department of Intensive Care Unit, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Mingmin Zhu
    School of Mathematics and Statistics, Xidian University, Xi'an, 710071, China.
  • Jiangtao Cui
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China.
  • Zhenzhen Liu
    Department of Functional Science, School of Medicine, Yangtze University, No.1 Nanhuan Road, Jingzhou City 434100, China.
  • Zhuoling Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Xiaoyan Li
    Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China.
  • Jingjing Chen
    Department of Cardiovascular Medicine, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
  • Qianzhong Cao
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Xiaohang Wu
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Dongni Wang
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Xian Lie South Road 54#, Guangzhou, 510060, China.
  • Jinghui Wang
    Shanxi Entry-Exit Inspection and Quarantine Bureau, Taiyuan, China.
  • Haotian Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou.

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