An Improved Semi-Supervised Learning Method on Cataract Fundus Image Classification

Song, Wenai; Cao, Ying; Qiao, Zhiqiang; Wang, Qing; Yang, Ji-Jiang

doi:10.1109/compsac.2019.10233

Cited by 20 publications

(13 citation statements)

References 16 publications

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“…The experimental results on the two public data sets of DR1 and MESSIDOR, show that the knowledge learned in other large data sets (source domain) can be used to obtain better classification in small data sets (target domain) through transfer learning. In 2019, Wenai Song et al proposed an improved semisupervised learning method for the damage effects of cataract diseases [26], which obtained additional information from unlabeled cataract fundus images and improved the accuracy of the basic model that only trained labeled images.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Multi-Label Classification of Fundus Images With EfficientNet

et al. 2020

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Convolutional neural network (CNN) has achieved remarkable success in the field of fundus images due to its powerful feature learning ability. Computer-aided diagnosis can obtain information with reference value for doctors in clinical diagnosis or screening through proper processing and analysis of fundus images. However, most of the previous studies have focused on the detection of a certain fundus disease, and the simultaneous diagnosis of multiple fundus diseases still faces great challenges. We propose a multi-label classification ensemble model of fundus images based on CNN to directly detect one or more fundus diseases in the retinal fundus images. Every single model consists of two parts. The first part is a feature extraction network based on EfficientNet, and the second part is a custom classification neural network for multi-label classification problems. Finally, the output probabilities of different models are fused as the final recognition result. And it was trained and tested on the data set provided by ODIR 2019 (Peking University International Competition on Ocular Disease Intelligent Recognition). The experimental results show that our model can be trained on fewer data sets and get good results.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Multi-Label Classification of Fundus Images With EfficientNet

et al. 2020

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“…In another approach, Song et al [35] proposed an improved semi-supervised learning method for extracting additional information from unlabelled cataract fundus images to improve the accuracy of the basic model trained exclusively on marker images. Semisupervised learning can improve performance by training the classifier with both labelled and unlabelled data.…”

Section: Cataract Detection With Machine Learning and Image Processingmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

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“…Literature [100,99] uses the semi-supervised learning strategy for cataract grading based on fundus images and achieves good results. It is likely to utilize weakly supervised learning to learn useful information from labeled cataract images and let the machine label unlabeled cataract images based on learned information automatically.…”

Section: Challenges and Possible Solutionsmentioning

confidence: 99%

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

Zhang¹,

Fang²,

Hu³

et al. 2020

Preprint

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Cataract is one of the leading causes of reversible visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art artificial intelligence techniques for automatic cataract classification and grading, helping clinicians prevent and treat cataract in time. This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning techniques and deep learning techniques. This paper also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification and grading based on machine learning techniques and present possible solutions to these challenges for future research.

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An Improved Semi-Supervised Learning Method on Cataract Fundus Image Classification

Cited by 20 publications

References 16 publications

Multi-Label Classification of Fundus Images With EfficientNet

Multi-Label Classification of Fundus Images With EfficientNet

Towards a Connected Mobile Cataract Screening System: A Future Approach

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

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