Glaucoma is a common eye disease that leads to irreversible blindness unless timely detected. Hence, glaucoma detection at an early stage is of utmost importance for a better treatment plan and ultimately saving the vision. The recent literature has shown the prominence of CNN-based methods to detect glaucoma from retinal fundus images. However, such methods mainly focus on solving binary classification tasks and have not been thoroughly explored for the detection of different glaucoma stages, which is relatively challenging due to minute lesion size variations and high inter-class similarities. This paper proposes a global self-attention based network called GS-Net for efficient multi-stage glaucoma classification. We introduce a global self-attention module (GSAM) consisting of two parallel attention modules, a channel attention module (CAM) and a spatial attention module (SAM), to learn global feature dependencies across channel and spatial dimensions. The GSAM encourages extracting more discriminative and class-specific features from the fundus images. The experimental results on a publicly available dataset demonstrate that our GS-Net outperforms state-of-the-art methods. Also, the GSAM achieves competitive performance against popular attention modules.
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）
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
International Medical College（国际医学院）；Chongqing Medical University（重庆医科大学）；The First Affiliated Hospital of Chongqing Medical University（重庆医科大学第一附属医院）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science Technology and Research (A*STAR)（新加坡科技研究局）
Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Guangzhou Shiyuan Electronic Technology Company Limited（广州视源电子科技股份有限公司）；Microsoft Research（微软研究院）；Singapore Eye Research Institute（新加坡眼科研究院）；Cixi Institute of Biomedical Engineering, CAS（中国科学院慈溪生物医学工程研究所）
GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining
Department of Computer Science, Southern Methodist University（计算机科学系，南方 Methodist 大学）；Department of Ophthalmology, University of Texas Southwestern Medical Center（眼科系，德克萨斯南方医学中心大学）；National Aeronautics and Space Administration（美国国家航空航天局）；Department of Pharmacy and Health Systems Sciences, Northeastern University（药学与健康系统科学系，东北大学）
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València（瓦伦西亚理工大学生物工程研究与创新研究所）；Universidad Politécnica de Cartagena（卡塔赫纳理工大学）
Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
University of Florida（佛罗里达大学）
眼科医生观察视网膜眼底图像是青光眼的主要诊断方法。然而，仅通过人工观察仍然难以区分病变的特征，特别是在青光眼早期。在本文中，我们提出了两种基于深度学习的自动算法，用于青光眼检测和视盘和视杯分割。我们利用注意力机制来学习像素级特征以进行准确预测。特别是，我们提出了两个卷积神经网络，可以专注于学习各种像素级特征。此外，我们还开发了几种注意力策略来指导网络学习对预测准确性有重大影响的重要特征。我们在验证数据集上评估了我们的方法，提出的两个任务的解决方案可以取得令人印象深刻的结果，并优于当前最先进的方法。\textit{The code is available at \url{https://github.com/cswin/RLPA}}。
AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Department of Computer Science, Columbia University, New York, NY, USA（计算机科学系，哥伦比亚大学）；Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA（眼科学系，哥伦比亚大学伊万杰琳医疗中心）；Department of Biomedical Engineering, Columbia University, New York, NY, USA（生物医学工程系，哥伦比亚大学）；The Data Science Institute, Columbia University, New York, NY, USA（数据科学学院，哥伦比亚大学）
Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore National Eye Centre（新加坡国家眼科中心）
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）
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）
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局资讯通信研究院）；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）；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）
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu Inc（百度公司）；Southern University of Science and Technology（南方科技大学）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Images
Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Eye Center（北京同仁眼科中心）；Beijing Tongren Hospital（北京同仁医院）；Capital Medical University（首都医科大学）；Beijing Key Laboratory of Ophthalmology and Visual Sciences（北京市眼科与视觉科学重点实验室）
Self-supervised denoising of visual field data improves detection of glaucoma progression
Pepperdine University（佩珀代因大学）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Stein Eye Institute（斯坦眼科研究所）；David Geffen School of Medicine（戴维·格芬医学院）；Columbia University（哥伦比亚大学）；University of California Riverside（加利福尼亚大学河滨分校）
Multimodal Information Fusion for Glaucoma and DR Classification
Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋国立高等工业技术学院）；Rothschild Foundation Hospital（罗斯柴尔德基金会医院）；Carl Zeiss Meditec Inc（卡尔蔡司医疗科技公司）；Lariboisière Hospital（拉里博瓦西耶医院）；CHRU Brest（布雷斯特大学中心医院）
Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；Baidu Inc（百度公司）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Southern University of Science and Technology（南方科技大学）；Inception Institute of Artificial Intelligence（启科人工智能研究院）
