Superimposition of eye fundus images for longitudinal analysis from large public health databases
International Prevention Research Institute（国际预防研究所）；Swansea University（斯旺西大学）；Diabetic Eye Screening Wales（威尔士糖尿病眼部筛查机构）；University of Strathclyde（思克莱德大学）
Guillaume Noyel, Rebecca Thomas, Gavin Bhakta, Andrew Crowder, David Owens, Peter Boyle
备注：
This is an author-created, un-copyedited version of an article published in Biomedical Physics \& Engineering Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2057-1976/aa7d16
In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is robustly fitted on salient points. Each stage involves linear estimators followed by non-linear optimisation. The model of image warping is also invertible for fast computation. The method has been validated (1) on a simulated montage and (2) on public health databases with 69 patients with high quality images (271 pairs acquired mostly with different types of camera and 268 pairs acquired mostly with the same type of camera) with success rates of 92% and 98%, and five patients (20 pairs) with low quality images with a success rate of 100%. Compared to two state-of-the-art methods, ours gives better results.
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
University of Bournemouth（伯恩茅斯大学）；École de Technolgie Superieure de Montréal（蒙特利尔高等技术学院）；Diagnos Inc.（Diagnos公司）；CRCHUM University of Montreal Hospital Centre（蒙特利尔大学医院研究中心）
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Beihang University（北京航空航天大学）；Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）；The University of Melbourne（墨尔本大学）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
Southern University of Science and Technology（南方科技大学）；The University of British Columbia（不列颠哥伦比亚大学）；The University of Hong Kong（香港大学）；The University of Queensland（昆士兰大学）
EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院北京同仁眼科中心）；Liangxiang Hospital of Beijing Fangshan District, Capital Medical University（首都医科大学北京市房山区良乡医院）；The Third People's Hospital of Dalian（大连市第三人民医院）；National Clinical Research Center for Endocrine and Metabolic Diseases, The Second Xiangya Hospital of Central South University（中南大学湘雅二医院国家内分泌代谢病临床医学研究中心）；The Central Hospital of Baoji City（宝鸡市中心医院）；Wuxi No.2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University（南通大学附属无锡临床学院无锡市第二人民医院）；Shenzhen Eye Hospital, Southern Medical University（南方医科大学深圳眼科医院）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）
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）
Deep learning for predicting refractive error from retinal fundus images
Google Research（谷歌研究院）；Google DeepMind（谷歌DeepMind）；NIHR Biomedical Research Centre for Ophthalmology（NIHR眼科生物医学研究中心）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院NHS基金会信托）；UCL Institute of Ophthalmology（伦敦大学学院眼科研究所）
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）
Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远景智能有限公司）；Peking Union Medical College Hospital（北京协和医院）；Chinese Academy of Medical Sciences（中国医学科学院）
A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks
Seoul National University（首尔大学）；Seoul National University Hospital（首尔大学附属医院）
SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention
School of Physics and Information Technology（物理与信息学院）；Shaanxi Normal University（陕西师范大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Central South University（中南大学）
DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
INESC TEC - Institute for Systems and Computer Engineering, Technology and Science（INESC TEC——系统与计算机工程、技术与科学研究院）；Faculty of Engineering of University of Porto（波尔图大学工程学院）；Hospital de Braga（布拉加医院）；Centro Hospitalar São João（圣若昂医院中心）；Faculty of Medicine of University of Porto（波尔图大学医学院）
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification
Sungkyunkwan University（成均馆大学）；College of Computing and Informatics, Sungkyunkwan University（成均馆大学计算与信息学院）；Dept. of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电气与计算机工程系）
Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans
School of Computer Science and Engineering, Southeast University（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（教育部新一代人工智能技术及其跨学科应用重点实验室）；Tianyuan Honors School, Nanjing Medical University（南京医科大学天元荣誉学院）；Nanjing University of Science and Technology（南京理工大学）；Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院眼科）
Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images
Department of Electrical \& Software Engineering, University of Calgary, Canada.；Department of Computer Science；Physics, Wilfrid Laurier University, Waterloo, Canada.；Dept of Ophthalmology \& Visual Sciences, University of Alberta, Edmonton, Canada.；Wills Eye Hospital, Philadelphia, USA.；Department of Surgery, University of Calgary, Canada.
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）
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Google AI（谷歌人工智能）；Google（谷歌公司）；Rajavithi Hospital（拉差威提医院）；College of Medicine, Rangsit University（兰实大学医学院）；EyePACS LLC（EyePACS有限责任公司）；University of California（加利福尼亚大学）；Google DeepMind（谷歌DeepMind）；NIHR Biomedical Research Centre for Ophthalmology（国立卫生研究院眼科生物医学研究中心）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院英国国民保健制度基金会信托）；UCL Institute of Ophthalmology（伦敦大学学院眼科研究所）
Disentangling representations of retinal images with generative models
Hertie Institute for AI in Brain Health, Faculty of Medicine, University of Tübingen（人工智能与脑健康赫特研究所，医学院，图宾根大学）；Tübingen AI Center, Tübingen, Germany（图宾根人工智能中心，图宾根，德国）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism UDEM, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland（糖尿病、内分泌学、营养医学和代谢学部门UDEM，伯尔尼大学医院，伯尔尼大学，伯尔尼，瑞士）；Department of Computer Science, University of Tübingen, Tübingen, Germany（计算机科学系，图宾根大学，图宾根，德国）
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
Beijing Institute of Technology（北京理工大学）；Beijing Tongren Hospital（北京同仁医院）；Capital Medical University（首都医科大学）；Beijing Institute of Ophthalmology（北京眼科研究所）
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
Weill Cornell Medicine（威尔康奈尔医学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Washington University School of Medicine（华盛顿大学医学院）；Columbia University（哥伦比亚大学）；National Eye Institute（国家眼科研究所）；National Institutes of Health (NIH)（美国国立卫生研究院）；National Center for Biotechnology Information (NCBI)（国家生物技术信息中心）；National Library of Medicine (NLM)（国家医学图书馆）
