Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of Retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to Retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the proposed approach can achieve superior performance in recognizing Retinal OCT images, even when dealing with exceedingly restricted labeled datasets. Furthermore, this method obviates the necessity of learning extensive-scale parameters, making it well-suited for deployment in low-resource scenarios.
University of Nevada, Reno（内华达大学里诺分校）；Houston Eye Associates（休斯顿眼科协会）
Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases. Additionally, reliance on large annotated datasets and lack of knowledge distillation can restrict ML-based clinical support systems' deployment in real-world environments. To improve the robustness and transferability of knowledge, an enhanced feature-learning module is required to extract meaningful spatial representations from the retinal subspace. Such a module, if used effectively, can detect unique disease traits and differenti…
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present …
The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm …
Monash University（莫纳什大学）；Airdoc；Kyushu Institute of Technology（九州工业大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD and DR share some common features like hemorrhagic points and exudation but most classification algorithms only train those disease models independently. Inspired by knowledge distillation where additional monitoring signals from various sour…
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Ural Federal University Named after the First President of Russia B. N. Yeltsin（俄罗斯首任总统B.N.叶利钦命名的乌拉尔联邦大学）；Ophthalmosurgery Clinic "Professorskaya Plus"（“Professorskaya Plus”眼科诊所）；Ural State Medical University（乌拉尔国立医科大学）
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Waterloo（滑铁卢大学）；Bern University Hospital（伯尔尼大学医院）；Tri-Service General Hospital（三军总医院）；National Taiwan University（台湾大学）
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina dataset, called EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the m…
The College of New Jersey（新泽西学院）；University of Oxford（牛津大学）；Ludwig Institute for Cancer Research（路德维希癌症研究所）
Disease complications can alter vascular network morphology and disrupt tissue functioning. Diabetic retinopathy, for example, is a complication of types 1 and 2 diabetes mellitus that can cause blindness. Microvascular diseases are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. We apply our methods …
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）；VoxelCloud, Inc.（体素云公司）
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel…
Airdoc LLC（鹰瞳科技）；Monash University（莫纳什大学）；Harbin Engineering University（哈尔滨工程大学）；The University of Sydney（悉尼大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which results in a challenging imbalance learning scenario. For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models. In this study, we propose class subset learning by dividing the long-tailed d…
Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation
University of Ibadan（伊巴迪安大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；University College London（伦敦大学学院）；Hertie Institute for Brain Health（海德研究所）
School of Mechanical, Electrical and Information Engineering, Shandong University（山东大学机电与信息工程学院）；School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；School of Computer Science and Technology, Zhejiang Sci-Tech University（浙江理工大学计算机科学与技术学院）；College of Media Engineering, Communication University of Zhejiang（浙江传媒学院媒体工程学院）；School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学网络空间学院）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
Georgia Institute of Technology（佐治亚理工学院）；University of Cambridge（剑桥大学）；Bern University Hospital（伯尔尼大学医院）；National Taiwan University（台湾大学）；University of California, Berkeley（加州大学伯克利分校）；Tri-Service General Hospital（三军总医院）；Beijing Friendship Hospital（北京友谊医院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologis…
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited number of samples), which results in a challenging long-tailed learning scenario. Some recently published datasets in ophthalmology AI consist of more than 40 kinds of retinal diseases with complex abnormalities and variable morbidity. Nevertheless, more than 30 conditions are rarely seen in global patient cohorts. From a modeling perspective, most deep learning models trained on these datasets may lack the ability to gen…
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd（远见智能有限公司）；Peking Union Medical College Hospital（北京协和医院）；Beijing Tongren Hospital（北京同仁医院）；Qingdao University（青岛大学）
Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We pro…
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
University of Ibadan（伊巴丹大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Hertie Institute for AI in Brain Health（赫蒂脑健康人工智能研究所）；University of Tübingen（蒂宾根大学）；University of Bern（伯尔尼大学）；University College London（伦敦大学学院）；Tübingen AI Center（蒂宾根人工智能中心）
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University School of Medicine（维克森林大学医学院）；Wake Forest School of Medicine（维克森林大学医学院）；University of Nottingham（诺丁汉大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imaging reference to detect eye-related pathology and predict longitudinal progression of the disease characteristics. Retina layer segmentation is one of the crucial feature extraction techniques, where the variations of retinal layer thicknesses and the retinal layer deformation due to the presence of the fluid are highly correlated with multiple epidemic eye diseases like Diabetic Retinopathy(DR) and Age-related Macular…
Center for Machine Vision and Signal Analysis (CMVS)（机器视觉与信号分析中心）；University of Oulu（奥卢大学）；Department of Eye and Vision Science（眼科学与视觉科学系）；University of Liverpool（利物浦大学）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
Hong Kong University of Science and Technology（香港科技大学）；Guangdong Weiren Meditech Co., Ltd.（广东伟仁医疗科技有限公司）；Yunnan United Vision Innovations Technology Co., Ltd.（云南联合视力创新科技有限公司）；Guangdong Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Second People’s Hospital of Foshan（佛山市第二人民医院）
RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Mayo Clinic（梅奥诊所）；School of Computing, Clemson University（克莱姆森大学计算学院）；Department of Electrical and Computer Engineering, Stevens Institute of Technology（史蒂文斯理工学院电气与计算机工程系）
Eötvös Loránd University（罗兰大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Jiangxi Normal University（江西师范大学）；Chinese Academy of Science（中国科学院）；Qinghai University（青海大学）
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure. In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation. RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting. Moreover, in order to further improve the representation capability of t…
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
Harvard Medical School（哈佛医学院）；Massachusetts Eye and Ear（麻省眼耳医院）；Schepens Eye Research Institute（谢彭斯眼科研究所）；Eye and Vision Research Institute（眼科与视觉研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Automated Multi-label Classification of Eleven Retinal Diseases: A Benchmark of Modern Architectures and a Meta-Ensemble on a Large Synthetic Dataset
Department of Cell Biology, University of Virginia, Charlottesville, VA, USA（细胞生物学系，弗吉尼亚大学）；Department of Ophthalmology, University of Virginia, Charlottesville, VA, USA（眼科学系，弗吉尼亚大学）；Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA（生物医学工程系，弗吉尼亚大学）；Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA（生物统计学系，北卡罗来纳大学教堂山分校）；Department of Oncology, People’s Hospital of Chongqing, Hechuan, Chongqing, China（肿瘤科，重庆人民医院）；Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA（电气与计算机工程系，旧 Dominion 大学）
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
The University of Queensland（昆士兰大学）；University of Technology Sydney（悉尼科技大学）；Australian National University（澳大利亚国立大学）；University of New South Wales（新南威尔士大学）；Victoria University（维多利亚大学）；Deakin University（迪肯大学）
Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. Many current CNN-based deep neural networks used for diagnosing retinal disorders might have appreciable performance but fail to pinpoint the basis driving their decisions. To improve these decisi…
Center for Cognitive Skill Enhancement（认知技能提升中心）；Independent University Bangladesh（孟加拉国独立大学）；University of Nevada, Reno（内华达大学雷诺分校）
Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning algorithms have been used for early detection and diagnosis of retinal diseases. Unfortunately, these are prone to error and computational inefficiency, which requires further intervention from human experts. In this paper, we propose a novel convolution neural network architecture to successfully distinguish between different degeneration of retinal layers and their underlying …
Khulna University of Engineering and Technology（库尔纳工程技术大学）
In medical science, the use of computer science in disease detection and diagnosis is gaining popularity. Previously, the detection of disease used to take a significant amount of time and was less reliable. Machine learning (ML) techniques employed in recent biomedical researches are making revolutionary changes by gaining higher accuracy with more concise timing. At present, it is even possible to automatically detect diseases from the scanned images with the help of ML. In this research, we have taken such an attempt to detect retinal diseases from optical coherence tomography (OCT) X-ray i…
Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to color fundus images for semantic segmentation of the blood vessels and severity classification on both vascular and full images. Vessel reconstruction through harmonic descriptors is also used as a smoothing and de-noising tool. The mathematical background of the theory is also outlined. Results: For diabetic patients, at least 93.8% of DR No-Refer vs. Refer classifica…
FaraAI Company（法拉人工智能公司）；University of Tehran（德黑兰大学）
Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the l…
Optical coherence tomography (OCT) scanning is useful in detecting various retinal diseases. However, there are not enough ophthalmologists who can diagnose retinal OCT images in much of the world. To provide OCT screening inexpensively and extensively, an automated diagnosis system is indispensable. Although many machine learning techniques have been presented for assisting ophthalmologists in diagnosing retinal OCT images, there is no technique that can diagnose independently without relying on an ophthalmologist, i.e., there is no technique that does not overlook any anomaly, including unle…
Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes
Laboratoire LIMOS, CNRS UMR 6158（利摩日大学实验室，法国国家科研中心UMR 6158联合研究单位）；Université Clermont-Auvergne（克莱蒙奥弗涅大学）；AL Mansour University College（曼苏尔大学学院）
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
A General Bézier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis
Department of Electronic and Electrical Engineering（电子与电气工程系）；School of Data Science（数据科学学院）；Research Institute of Electrical Communication（电气通信研究所）；J. Crayton Pruitt Family Department of Biomedical Engineering（姜·克雷顿·普瑞特家庭生物医学工程系）
University of Nevada, Reno（内华达大学雷诺分校）；Houston Eye Associates（休斯顿眼科协会）
Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases. In addition, the lack of knowledge transferability and the need for unreasonably large datasets limit clinical application of current machine learning systems. To increase robustness, a better understanding of how the retinal subspace deformations lead to various levels of disease severity needs to be utilized for prioritizing disease-specific model details. In this paper we propose the use of diseas…
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）
University of Nevada, Reno（内华达大学雷诺分校）；Houston Eye Associates（休斯顿眼科协会）
In Fluorescein Angiography (FA), an exogenous dye is injected in the bloodstream to image the vascular structure of the retina. The injected dye can cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even death. In contrast, color fundus imaging is a non-invasive technique used for photographing the retina but does not have sufficient fidelity for capturing its vascular structure. The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA). However, OCTA equipment is quite expensive, and stable imaging is limited to s…
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive capabilities of several heterogeneous deep convolutional neural network models. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and Focal loss utiliza…
Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Monash University（莫纳什大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）；The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼与视觉研究中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Airdoc-Monash Research Lab（Airdoc-莫纳什研究实验室）
University of Rostock（罗斯托克大学）；Rostock University Medical Center（罗斯托克大学医学中心）
Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We prese…
Deep learning methods for ophthalmic diagnosis have shown considerable success in tasks like segmentation and classification. However, their widespread application is limited due to the models being opaque and vulnerable to making a wrong decision in complicated cases. Explainability methods show the features that a system used to make prediction while uncertainty awareness is the ability of a system to highlight when it is not sure about the decision. This is one of the first studies using uncertainty and explanations for informed clinical decision making. We perform uncertainty analysis of a…
The Pingry School（平格里学校）；Chinle Comprehensive Health Care Facility（钦利综合医疗中心）；University of Maryland, College Park（马里兰大学帕克分校）；NVIDIA（英伟达）
30 million Optical Coherence Tomography (OCT) imaging tests are issued annually to diagnose various retinal diseases, but accurate diagnosis of OCT scans requires trained eye care professionals who are still prone to making errors. With better systems for diagnosis, many cases of vision loss caused by retinal disease could be entirely avoided. In this work, we present ReLaX, a novel deep learning framework for explainable, accurate classification of retinal pathologies which achieves state-of-the-art accuracy. Furthermore, we emphasize producing both qualitative and quantitative explanations o…
A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Khalifa University（哈利法大学）；National University of Sciences and Technology（巴基斯坦国立科技大学）；Margalla Institute of Health Sciences（马尔加拉健康科学学院）；Abu Dhabi University（阿布扎比大学）；Weill Cornell Medicine-Qatar（威尔康奈尔医学院卡塔尔分校）
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
Naif Arab University for Security Sciences（奈伊夫阿拉伯安全科学大学）；Charles Sturt University（查尔斯特大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks
Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk sco…
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.
