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Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey

Abstract

Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians’ diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 8210072776), Guangdong Provincial Department of Education, China (No. 2020ZD ZX3043), Guangdong Provincial Key Laboratory, China (No. 2020B121201001), and Shenzhen Natural Science Fund, China (No. JCYJ20200109140820699), and the Stable Support Plan Program, China (No. 20200925174052004).

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Correspondence to Jiang Liu.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Xiao-Qing Zhang received the B.Sc. degree in water conservancy and hydro-power engineering from South China Agricultural University, China in 2016, the M.Sc. degree in computer engineering from Zhengzhou University. Currently, he is a Ph.D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China.

His research interests include deep learning, interpretability, and medical image processing.

Yan Hu received the B. Sc. and M. Sc. degrees in computer science from Northeast Normal University, China in 2008 and 2011, respectively, and the Ph. D. degree in optics from University of Tokyo, Japan in 2016. Currently, she is a research assistant professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China.

Her research interests include surgical assistance, intraoperative navigation, and medical image processing.

Zun-Jie Xiao received the B. Sc. degree in bioinformatics from Dalian University of Technology, China in 2019. Currently, he is a Ph.D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China.

His research interests include deep learning and medical image processing.

Jian-Sheng Fang received the B.Sc. degree in computer science and technology from Harbin Institute of Technology, China, the M.Sc. degree in computer application from Sun Yat-sen University, China. He is a Ph.D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China.

His research interests include deep learning and image retrieval.

Risa Higashita received the Ph.D. degree in biomedical engineering from Nagoya University, Japan in 2004. Currently, she is a visiting professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China.

Her research interests include medical image processing and ophthalmology.

Jiang Liu received the B.Sc. degree in computer science from University of Science and Technology of China, China in 1988, the M.Sc. and Ph.D. degrees from National University of Singapore, Singapore in 1992 and 2004, respectively. He founded the Intelligent Medical Imaging Research Team which was once the world’s largest ophthalmic medical image processing team, focusing on ophthalmic artificial intelligence research. Currently, he is a professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China.

His research interests include artificial intelligence, eye-brain research, precision medicine, and surgical robots.

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Zhang, XQ., Hu, Y., Xiao, ZJ. et al. Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey. Mach. Intell. Res. 19, 184–208 (2022). https://doi.org/10.1007/s11633-022-1329-0

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Keywords

  • Cataract
  • classification and grading
  • ophthalmic image
  • machine learning
  • deep learning.