This paper proposes the importance of age and gender information in the diagnosis of diabetic retinopathy. We utilized Deep Residual Neural Networks (ResNet) and Densely Connected Convolutional Networks (DenseNet), which are proven effective on image classification problems and the diagnosis of diabetic retinopathy using the retinal fundus images. We used the ensemble of several classical networks and decentralized the training so that the network was simple and avoided overfitting. To observe whether the age and gender information could help enhance the performance, we added the information before the dense layer and compared the results with the results that did not add age and gender information. We found that the test accuracy of the network with age and gender information was 2.67% higher than that of the network without age and gender information. Meanwhile, compared with gender information, age information had a better help for the results.
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Carnegie Mellon University（卡内基梅隆大学）；INESC TEC, Porto（波尔图INESC TEC研究所）；École de Tecnologie Superieure, Montreal（蒙特利尔高等技术学院）；Faculty of Engineering, University of Porto（波尔图大学工程学院）
Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；“Iuliu Hatieganu” University of Medicine and Pharmacy（“尤利乌·哈采加努”医药大学）；Emergency County Hospital（急诊县医院）
Optical Coherence Tomography Angiography-OCTA dataset for the study of Diabetic Retinopathy
Symbiosis Institute of Technology, Symbiosis International (Deemed University)（共生国际（ deemed 大学）共生技术学院）；University of Technology Sydney（悉尼科技大学）；Natasha Eye Care（娜塔莎眼科护理中心）
Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）；National University of Singapore（新加坡国立大学）
A multi-center prospective evaluation of THEIA to detect diabetic retinopathy (DR) and diabetic macular edema (DME) in the New Zealand screening program
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
High Technology High School（高新技术高中）；Massachusetts Eye and Ear Harvard Medical School（马萨诸塞眼耳医院哈佛医学院）；Harvard Medical School（哈佛医学院）；Harvard Ophthalmology AI Lab（哈佛眼科人工智能实验室）
Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心（DFKI））；Osaka Prefecture University（大阪府立大学）；National University of Science and Technology (NUST)（国立科技大学（NUST））；Opthalmology Clinic（眼科诊所）
DiaRet: A browser-based application for the grading of Diabetic Retinopathy with Integrated Gradients
Netaji Subhas Institute of Technology（内塔吉·苏巴斯技术学院）；Pandit Deendayal Energy University（潘迪特·迪恩达亚尔能源大学）；National Institute of Technology Delhi（德里国立理工学院）；AMC MET Medical College（AMC MET医学院）
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy
LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101实验室）；University of Western Brittany（西布列塔尼大学）；University of Electronic Science and Technology of China（电子科技大学）；IMT Atlantique（大西洋国立高等矿业电信学院）
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
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）；Institute of Systems Science, National University of Singapore（新加坡国立大学系统科学研究院）
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；VUNO Inc.（VUNO公司）；Johannes Kepler University Linz（林茨约翰·开普勒大学）；Fudan University（复旦大学）；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）
Classification of Diabetic Retinopathy Severity in Fundus Images with DenseNet121 and ResNet50
Commack High School（康马克高中）；Camp Illumina（Illumina 训练营）；Glenda Dawson High School（格伦达·道森高中）；Mira Loma High School（米拉洛马高中）；Ward Melville High School（沃德·梅尔维尔高中）
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
Inception Institute of Artificial Intelligence（因赛普申人工智能研究院）；Beijing BeYes Technology Co. Ltd.（北京贝叶斯科技有限公司）
糖尿病视网膜病变（DR）是糖尿病的一种并发症，严重影响眼睛。根据国际协议，它可以分为五个严重程度。然而，优化分级模型以具有较强的泛化能力需要大量平衡的训练数据，这对于高严重性级别来说尤其难以收集。典型的数据增强方法，包括随机翻转和旋转，不能生成具有高多样性的数据。在本文中，我们提出了一个糖尿病视网膜病变生成对抗网络（DR-GAN）合成高分辨率眼底图像，可以与任意分级和病变信息进行操作。因此，大规模生成的数据可以用于更有意义的增强，以训练DR分级和病变分割模型。建议的视网膜发生器的条件上的结构和病变掩模，以及从潜在的分级空间，它可以被用来控制合成的分级严重性采样的自适应分级向量。此外，设计了多尺度空间和通道注意模块，以提高合成细节的生成能力。多尺度鉴别器被设计成从大到小的感受野操作，并且采用联合对抗损失以端到端的方式优化整个网络。通过在连接到Kaggle的EyePACS数据集以及FGADR数据集上进行广泛的实验评估，我们验证了我们的方法的有效性，该方法既可以合成高度逼真（1280 x 1280）的可控眼底图像，又有助于DR分级任务。
Uncertainty-Aware Ordinal Deep Learning for cross-Dataset Diabetic Retinopathy Grading
Department of Computer Science & Engineering, Mississippi State University, MS, USA（计算机科学与工程系，密苏里州立大学）；Department of Mechanical Engineering, Mississippi State University, MS, USA（机械工程系，密苏里州立大学）；College of Engineering & Architecture, International University of Rabat（工程与建筑学院，拉巴特国际大学）
Explainable Diabetic Retinopathy Detection and Retinal Image Generation
Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）；RIKEN AIP（理化学研究所人工智能研究中心）；University of Tokyo（东京大学）；Ningbo Institute of Industrial Technology, CAS（中国科学院宁波工业技术研究院）
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Institute of Systems Science, National University of Singapore（新加坡国立大学系统科学研究所）；University of Electronic Science and Technology of China（电子科技大学）；Wuhan University of Science and Technology（武汉科技大学）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System（湖北省智能信息处理与实时工业系统重点实验室）
FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy
Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal（计算机科学与工程系，穆拉纳·阿布·卡尔·拉马纳特技术大学，西孟加拉邦）；Department of Computer Science and Engineering, MCKV Institute of Engineering, West Bengal（计算机科学与工程系，MCKV工程学院，西孟加拉邦）；Department of Computer Science and Engineering, OmDayal Group of Institutions, West Bengal（计算机科学与工程系，OmDayal学院集团，西孟加拉邦）
Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading
School of Electronic and Information Engineering, Guangxi Normal University（广西师范大学电子与信息工程学院）；School of Computer Science and Engineering, Guangxi Normal University（广西师范大学计算机科学与工程学院）；University of Cambridge（剑桥大学）
