A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets
Abstract Background Accurate detection of glaucoma plays a critical role in treating the disease and can be performed on limited labeled retinal fundus images and large-scale unlabeled ones leveraging a deep semi-supervised learning (SSL) technology. This study aims to investigate how glaucoma depic...
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BMC
2025-05-01
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| Series: | BMC Ophthalmology |
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| Online Access: | https://doi.org/10.1186/s12886-025-04153-1 |
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| author | Lei Wang Xiaoyun Zhang Zhongwen Li Shuchen Yu Yabo Wu Shaodan Zhang Gaoqiang Jiang Bihan Tian Chenyang Mei Jiantao Pu Yuanbo Liang Quanyong Yi Wencan Wu |
| author_facet | Lei Wang Xiaoyun Zhang Zhongwen Li Shuchen Yu Yabo Wu Shaodan Zhang Gaoqiang Jiang Bihan Tian Chenyang Mei Jiantao Pu Yuanbo Liang Quanyong Yi Wencan Wu |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | Abstract Background Accurate detection of glaucoma plays a critical role in treating the disease and can be performed on limited labeled retinal fundus images and large-scale unlabeled ones leveraging a deep semi-supervised learning (SSL) technology. This study aims to investigate how glaucoma depicted on fundus images can be reliably detected by the SSL technology and the impact of the quantities and qualities of unlabeled images on the outcome. Methods We retrospectively collected a dataset consisting of 7,503 fundus images and classified them into four categories, namely none, mild, moderate, or severe glaucoma. We used the collected dataset and a public out-of-distribution (OOD) dataset (EyeQ) to train an available SSL method (called SRC-MT) to grade glaucoma. Results SRC-MT achieved an average area under the receiver operating characteristic curve (AUC) of 0.8944 and 0.8969 on global field-of-view (FOV) regions and local disc regions, respectively when trained on 600 labeled images and 5401 unlabeled ones from the collected dataset. When separately introducing 16,817, 6,435, and 5,540 unlabeled OOD images with the qualities of ‘good’, ‘usable’, and ‘reject’ from the EyeQ dataset into 5,401 unlabeled images, its performance became 0.8972, 0.8908, and 0.8922, respectively for global FOV regions on the testing subset from the collected dataset, and 0.7342, 0.5090, and 0.5072 on three public datasets (i.e., AIROGS, EDDFS, and FIVES). Conclusions SRC-MT achieved promising performance for glaucoma grading, especially in global FOV regions. Its performance increased when using more labeled images, but degraded when using more unlabeled OOD images with worse image qualities. |
| format | Article |
| id | doaj-art-11bc6c6cab7e4d0c8f644d0b23308375 |
| institution | DOAJ |
| issn | 1471-2415 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Ophthalmology |
| spelling | doaj-art-11bc6c6cab7e4d0c8f644d0b233083752025-08-20T03:22:08ZengBMCBMC Ophthalmology1471-24152025-05-0125111710.1186/s12886-025-04153-1A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasetsLei Wang0Xiaoyun Zhang1Zhongwen Li2Shuchen Yu3Yabo Wu4Shaodan Zhang5Gaoqiang Jiang6Bihan Tian7Chenyang Mei8Jiantao Pu9Yuanbo Liang10Quanyong Yi11Wencan Wu12National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityThe University of SydneyThe Affiliated Ningbo Eye Hospital of Wenzhou Medical UniversityNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversitySchool of Biomedical Engineering and Imaging Sciences, King’s college LondonNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityDepartments of Radiology and Bioengineering, University of PittsburghNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityThe Affiliated Ningbo Eye Hospital of Wenzhou Medical UniversityNational Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical UniversityAbstract Background Accurate detection of glaucoma plays a critical role in treating the disease and can be performed on limited labeled retinal fundus images and large-scale unlabeled ones leveraging a deep semi-supervised learning (SSL) technology. This study aims to investigate how glaucoma depicted on fundus images can be reliably detected by the SSL technology and the impact of the quantities and qualities of unlabeled images on the outcome. Methods We retrospectively collected a dataset consisting of 7,503 fundus images and classified them into four categories, namely none, mild, moderate, or severe glaucoma. We used the collected dataset and a public out-of-distribution (OOD) dataset (EyeQ) to train an available SSL method (called SRC-MT) to grade glaucoma. Results SRC-MT achieved an average area under the receiver operating characteristic curve (AUC) of 0.8944 and 0.8969 on global field-of-view (FOV) regions and local disc regions, respectively when trained on 600 labeled images and 5401 unlabeled ones from the collected dataset. When separately introducing 16,817, 6,435, and 5,540 unlabeled OOD images with the qualities of ‘good’, ‘usable’, and ‘reject’ from the EyeQ dataset into 5,401 unlabeled images, its performance became 0.8972, 0.8908, and 0.8922, respectively for global FOV regions on the testing subset from the collected dataset, and 0.7342, 0.5090, and 0.5072 on three public datasets (i.e., AIROGS, EDDFS, and FIVES). Conclusions SRC-MT achieved promising performance for glaucoma grading, especially in global FOV regions. Its performance increased when using more labeled images, but degraded when using more unlabeled OOD images with worse image qualities.https://doi.org/10.1186/s12886-025-04153-1Glaucoma detectionFundus imagesSemi-supervised learningOut-of-distributionQuantities and qualities of images |
| spellingShingle | Lei Wang Xiaoyun Zhang Zhongwen Li Shuchen Yu Yabo Wu Shaodan Zhang Gaoqiang Jiang Bihan Tian Chenyang Mei Jiantao Pu Yuanbo Liang Quanyong Yi Wencan Wu A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets BMC Ophthalmology Glaucoma detection Fundus images Semi-supervised learning Out-of-distribution Quantities and qualities of images |
| title | A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets |
| title_full | A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets |
| title_fullStr | A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets |
| title_full_unstemmed | A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets |
| title_short | A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets |
| title_sort | deep semi supervised learning approach to the detection of glaucoma on out of distribution retinal fundus image datasets |
| topic | Glaucoma detection Fundus images Semi-supervised learning Out-of-distribution Quantities and qualities of images |
| url | https://doi.org/10.1186/s12886-025-04153-1 |
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