With the advancement of powerful image processing and machine learning techniques, CAD has become ever more prevalent in all fields of medicine including ophthalmology. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on RCNN and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep CNN to classify the extracted disc into healthy or glaucomatous. In addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved AUC equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA. Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only AUC, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier's performance and calls for additional performance metrics to substantiate the results.
Deep learning on fundus images detects glaucoma beyond the optic disc
KU Leuven（鲁汶大学）；UZ Leuven（鲁汶大学医院）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；Hasselt University（哈瑟尔特大学）；University of Antwerp（安特卫普大学）；Flemish Institute for Technological Research（弗拉芒技术研究院）
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学技术研究委员会）；UNICEN（阿根廷中央国立大学）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Tencent（腾讯）
Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image
Institute for Infocomm Research, Agency for Science, Technology and Research（科学、技术和研究局信息通信研究所）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；CVTE Research, Guangzhou Shiyuan Electronic Technology Company Limited（广州视源电子科技股份有限公司CVTE研究院）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
Medical University of Vienna（维也纳医科大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；University of Porto（波尔图大学）；KU Leuven（鲁汶大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；University of Florida（佛罗里达大学）；The Chinese University of Hong Kong（香港中文大学）；Gachon University（嘉泉大学）；Samsung SDS AI Research Center（三星SDS人工智能研究中心）；Yale University（耶鲁大学）
Learning to screen Glaucoma like the ophthalmologists
Baidu, Healthcare Group（百度医疗健康集团）；State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心眼科学国家重点实验室）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
Ahsanullah University of Science and Technology（阿萨努拉科技大学）
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Guangdong Weiren Meditech Co., Ltd（广东唯仁医疗科技有限公司）
Retinal Layer Segmentation in OCT Images With 2.5D Cross-slice Feature Fusion Module for Glaucoma Assessment
Department of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea（延世大学机械工程系）；Institute of Vision Research, Department of Ophthalmology, Severance Hospital, Yonsei University, Seoul, 03722, South Korea（视觉研究所眼科部, 顺化医院, 延世大学, 首尔, 03722, 韩国）；Yonsei University College of Medicine, Seoul, 03722, South Korea（延世大学医学院）
MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, China（教育部新一代人工智能技术及其交叉应用重点实验室）；Nanjing University of Science and Technology, Nanjing, China（南京理工大学）；Agency for Science, Technology and Research (A*STAR), Singapore（新加坡科技研究局）
尽管贫血的患病率很高，但由于筛查和诊断测试的侵入性和成本，贫血往往未被发现。虽然已经开发了一些非侵入性方法，但它们不如侵入性方法准确，导致对更准确的非侵入性方法的需求未得到满足。在这里，我们展示了基于深度学习的算法可以检测贫血，并使用视网膜眼底图像单独或与患者人口统计学等基本元数据相结合来量化几个相关的血液测量值。在来自英国生物库的11，388名患者的验证数据集上，我们的算法在定量血红蛋白浓度方面实现了0.63 g/dL的平均绝对误差（95%置信区间（CI）0.62-0.64），在检测贫血方面实现了0.88（95% CI 0.86-0.89）的受试者工作特征曲线下面积（AUC）。这项工作显示了基于眼底图像的自动化非侵入性贫血筛查的潜力，特别是在糖尿病患者中，这些患者可能定期进行视网膜成像，并且贫血进一步发病和死亡的风险增加。
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases（眼科学国家重点实验室、中山眼科中心、中山大学、广东省眼科学重点实验室和视觉科学、广东省眼科临床研究中心）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Hainan International College, Minzu University of China（海南国际学院，中国民族大学）；School of Information Technology, Faculty of Business and Hospitality, Torrens University Australia（澳大利亚托伦斯大学信息科技学院，商业与酒店管理学院）；AIM for Health Lab, Faculty of IT, Monash University, Australia（健康AIM实验室，墨尔本大学IT学院，澳大利亚）；Department of Ophthalmology, Guangzhou First People’s Hospital, the Second Affiliated Hospital of South China University of Technology（广州第一人民医院，华南理工大学第二附属医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）
Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images
Division of Engineering Science（工程科学系）；University of Toronto（多伦多大学）；School of Information Technology（信息科技学院）；King Mongkut’s University of Technology Thonburi（泰国科技泰伦比大学）；Innovative Cognitive Computing Research Center（创新认知计算研究中心）；Chinese International School of Hong Kong（香港中文国际学校）
