Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer–Based Learning Across Multiple Color-Spaces-Reference-Cited by-同舟云学术

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer–Based Learning Across Multiple Color-Spaces

Published:2024-04-03 Issue:4 Volume:13 Page:8
ISSN:2164-2591
Container-title:Translational Vision Science & Technology
language:en
Short-container-title:Trans. Vis. Sci. Tech.

Author:

Huang Chengcheng¹,Jiang Yukang²,Yang Xiaochun³,Wei Chiyu¹,Chen Hongyu⁴,Xiong Weixue¹,Lin Henghui¹,Wang Xueqin⁵,Tian Ting²,Tan Haizhu¹

Affiliation:

1. Department of Preventive Medicine, Shantou University Medical College, Shantou, China

2. School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, China

3. The First People's Hospital of Yun Nan Province, Kunming, China

4. Department of Optoelectronic Information Science and Engineering, Physical and Materials Science College, Guangzhou University, Guangzhou, China

5. School of Management, University of Science and Technology of China, Hefei, Anhui, China

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Reference37 articles.

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4. Identification of suitable fundus images using automated quality assessment methods;Sevik;J Biomed Opt,Apr 2014

5. Suitability of UK Biobank retinal images for automatic analysis of morphometric properties of the vasculature;MacGillivray;PLoS One,2015