Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset. Issue 3 (18th May 2022)

• Record Type: Journal Article
• Authors: Zheng, Ce; Ye, Hongfei; Yang, Jianlong; Fei, Ping; Qiu, Yingping; Xie, Xiaolin; Wang, Zilei; Chen, Jili; Zhao, Peiquan
• Is Part Of: Asia-Pacific journal of ophthalmology. Volume 11:Issue 3(2022)
• Page Start: 219
• Page End: 226
• Publication Date: 2022-05-18
• Subjects: deep learning -- generative adversarial networks -- optical coherence tomography -- retinal disorders -- semi-supervised; Ophthalmology -- Periodicals; Eye -- Diseases -- Periodicals; Periodicals; 617.7005
• DOI: 10.1097/APO.0000000000000498
• Languages: English
• ISSNs: 0129-1653

Abstract

Abstract : Purpose: To develop and test semi-supervised generative adversarial networks (GANs) that detect retinal disorders on optical coherence tomography (OCT) images using a small-labeled dataset. Methods: From a public database, we randomly chose a small supervised dataset with 400 OCT images (100 choroidal neovascularization, 100 diabetic macular edema, 100 drusen, and 100 normal) and assigned all other OCT images to unsupervised dataset (107, 912 images without labeling). We adopted a semi-supervised GAN and a supervised deep learning (DL) model for automatically detecting retinal disorders from OCT images. The performance of the 2 models was compared in 3 testing datasets with different OCT devices. The evaluation metrics included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves. Results: The local validation dataset included 1000 images with 250 from each category. The independent clinical dataset included 366 OCT images using Cirrus OCT Shanghai Shibei Hospital and 511 OCT images using RTVue OCT from Xinhua Hospital respectively. The semi-supervised GANs classifier achieved better accuracy than supervised DL model (0.91 vs 0.86 for local cell validation dataset, 0.91 vs 0.86 in the Shanghai Shibei Hospital testing dataset, and 0.93 vs 0.92 in Xinhua Hospital testing dataset). For detecting urgent referrals (choroidal neo-vascularization and diabetic macular edema) from nonurgent referrals (drusen and normal) on OCT images, the semi-supervised GANs classifier also achieved better area under the receiver operating characteristic curves than supervised DL model (0.99 vs 0.97, 0.97 vs 0.96, and 0.99 vs 0.99, respectively). Conclusions: A semi-supervised GAN can achieve better performance than that of a supervised DL model when the labeled dataset is limited. The current study offers utility to various research and clinical studies using DL with relatively small datasets. Semi-supervised GANs can detect retinal disorders from OCT images using relatively small dataset.