Strabismus Detection Based on Uncertainty Estimation and Knowledge Distillation

被引：0

作者：

Rong, Yibiao ^{[1
]}

Yang, Ziyin ^{[1
]}

Zheng, Ce ^{[2
]}

Fan, Zhun ^{[3
]}

机构：

[1] College of Engineering, Shantou University, Shantou

[2] Department of Ophthalmology, Xinhua Hospital, Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai

[3] Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen

来源：

Journal of Beijing Institute of Technology (English Edition) | 2024年 / 33卷 / 05期

关键词：

knowledge distillation; strabismus detection; uncertainty estimation;

D O I：

10.15918/j.jbit1004-0579.2024.058

中图分类号：

学科分类号：

摘要：

Strabismus significantly impacts human health as a prevalent ophthalmic condition. Early detection of strabismus is crucial for effective treatment and prognosis. Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely. This paper employed a Bayesian deep learning algorithm with knowledge distillation, improving the model's performance and uncertainty estimation ability. Trained on 6807 images from two tertiary hospitals, the model showed significantly higher diagnostic accuracy than traditional deep-learning models. Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability. These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation, which improve the reliability and accuracy of strabismus diagnostic predictions. © 2024 Beijing Institute of Technology. All rights reserved.

引用

页码：399 / 411

页数：12

共 38 条

[1] Repka M. X., Lum F., Burugapalli B., Strabismus, strabismus surgery, and reoperation rate in the united states: analysis from the iris registry, Ophthalmology, 125, 10, pp. 1646-1653, (2018)
[2] McKean-Cowdin R., Cotter S. A., Tarczy-Hornoch K., Wen G., Kim J., Borchert M., Varma R., Prevalence of amblyopia or strabismus in asian and non-hispanic white preschool children: multi-ethnic pediatric eye disease study, Ophthalmology, 120, 10, pp. 2117-2124, (2013)
[3] Chia A., Roy L., Seenyen L., Comitant horizontal strabismus: an asian perspective, British Journal of Ophthalmology, 91, 10, pp. 1337-1340, (2007)
[4] Choi R. Y., Kushner B. J., The accuracy of experienced strabismologists using the hirschberg and krimsky tests, Ophthalmology, 105, 7, pp. 1301-1306, (1998)
[5] Zheng C., Yao Q., Lu J., Xie X., Lin S., Wang Z., Wang S., Fan Z., Qiao T., Detection of referable horizontal strabismus in children’s primary gaze photographs using deep learning, Transational vision science and technology, 10, 1, pp. 33-33, (2021)
[6] Yang H. K., Seo J. M., Hwang J. M., Kim K. G., Automated analysis of binocular alignment using an infrared camera and selective wavelength filter, Investigative Ophthalmology & Visual Science, 54, 4, pp. 2733-2737, (2013)
[7] de Almeida J. D. S., Silva A. C., de Paiva A. C., Teixeira J. A. M., Computational methodology for automatic detection of strabismus in digital images through hirschberg test, Computers in Biology and Medicine, 42, 1, pp. 135-146, (2012)
[8] Hassanin M., Anwar S., Radwan I., Khan F. S., Mian A., Visual attention methods in deep learning: An in-depth survey, Information Fusion, 108, (2024)
[9] Ahmed S. F., Alam M. S. B., Hassan M., Rozbu M. R., Ishtiak T., Rafa N., Mofijur M., Shawkat Ali A., Gandomi A. H., Deep learning modelling techniques: current progress, applications, advantages, and challenges, Artificial Intelligence Review, 56, (2023)
[10] Sharifani K., Amini M., Machine learning and deep learning: A review of methods and applications, World Information Technology and Engineering Journal, 10, pp. 3897-3904, (2023)

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