The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline.
Sri Venkateswara college of Engineering（斯里文卡特斯瓦拉工程学院）
Teleophthalmology holds a great potential to improve the quality, access, and affordability in health care. For patients, it can reduce the need for travel and provide the access to a superspecialist. Ophthalmology lends itself easily to telemedicine as it is a largely image based diagnosis. The main goal of the proposed system is to diagnose the type of disease in the retina and to automatically detect and segment retinal diseases without human supervision or interaction. The proposed system will diagnose the disease present in the retina using a neural network based classifier.The extent of …
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 …
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…
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Khalifa University（哈利法大学）；National University of Sciences and Technology（国立科技大学）
Retinal lesions play a vital role in the accurate classification of retinal abnormalities. Many researchers have proposed deep lesion-aware screening systems that analyze and grade the progression of retinopathy. However, to the best of our knowledge, no literature exploits the tendency of these systems to generalize across multiple scanner specifications and multi-modal imagery. Towards this end, this paper presents a detailed evaluation of semantic segmentation, scene parsing and hybrid deep learning systems for extracting the retinal lesions such as intra-retinal fluid, sub-retinal fluid, h…
Beihang University（北京航空航天大学）；National Institute of Informatics（国立情报学研究所）；Peng Cheng Laboratory（鹏城实验室）；Central South University（中南大学）
Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based meth…
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）；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）；Shanghai University（上海大学）
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyse the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in t…
Centre Universitaire Condorcet（孔多塞大学中心）；Université de Bourgogne（勃艮第大学）
Diabetic retinopathy (DR) and age related macular degeneration (ARMD) are among the major causes of visual impairment worldwide. DR is mainly characterized by red spots, namely microaneurysms and bright lesions, specifically exudates whereas ARMD is mainly identified by tiny yellow or white deposits called drusen. Since exudates might be the only manifestation of the early diabetic retinopathy, there is an increase demand for automatic retinopathy diagnosis. Exudates and drusen may share similar appearances, thus discriminating between them is of interest to enhance screening performance. In t…
University of Waterloo（滑铁卢大学）；Manipal Institute of Technology（马尼帕尔理工学院）
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.
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…
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However, analysis of retinal OCT requires trained ophthalmologists and time, making a comprehensive early diagnosis unlikely. A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images. The performance of the tool in classifying retinal conditions was on par to that of trained medical experts. …
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 …
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…
JIS College of Engineering（JIS工程学院）；Kalyani Govt. Engineering College（卡利亚尼政府工程学院）
Segmentation of blood vessels in retinal images provides early diagnosis of diseases like glaucoma, diabetic retinopathy and macular degeneration. Among these diseases occurrence of Glaucoma is most frequent and has serious ocular consequences that can even lead to blindness, if it is not detected early. The clinical criteria for the diagnosis of glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defects. This form of blood vessel segmentation helps in early detection for ophthalmic diseases, and potentially reduces the ri…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；National Taiwan University（台湾大学）；University of Waterloo（滑铁卢大学）；University of California San Diego（加州大学圣地亚哥分校）；Georgia Institute of Technology（佐治亚理工学院）；Tri-Service General Hospital（三军总医院）；National Defense Medical Center（国防医学院）
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. We propose a novel visual-assisted diagnosis hybrid model based on the support vector machine (SVM) and deep neural networks (DNNs). The model incorporates complementary strengths of DNNs and SVM. Furthermore, we present a new clinical retina label collection for ophthalmology incorporating 32 retina diseases classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the model performance is comparable to the professional ophthal…
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）
Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis
Tianjin University（天津大学）；Tianjin Key Laboratory of Ophthalmology and Visual Science（天津眼科学与视觉科学重点实验室）；Tianjin Eye Institute（天津眼科研究院）；Tianjin Eye Hospital（天津眼科医院）；Clinical College of Ophthalmology, Tianjin Medical University（天津医科大学临床医学院）；Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University（南昌大学第二附属医院眼科部）；Nankai University Affiliated Eye Hospital（南开大学附属眼科医院）
Dept. Ophthalmology, Seoul Nat'l University Bundang Hospital , Dept. Electronic Eng., Soonchunhyang Univ.
Retinal images have the highest resolution and clarity among medical images. Thus, vessel analysis in retinal images may facilitate early diagnosis and treatment of many chronic diseases. In this paper, we propose a novel multi-scale residual convolutional neural network structure based on a \emph{scale-space approximation (SSA)} block of layers, comprising subsampling and subsequent upsampling, for multi-scale representation. Through analysis in the frequency domain, we show that this block structure is a close approximation of Gaussian filtering, the operation to achieve scale variations in …
In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel struc…
Full end-to-end diagnostic workflow automation of 3D OCT via foundation model-driven AI for retinal diseases
Sun Yat-sen University（中山大学）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Zhongshan City People's Hospital（中山市人民医院）；Guangdong Medical University（广东医科大学）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学附属第三医院）；Zhaoqing Gaoyao People’s Hospital（肇庆高要人民医院）；Tianjin Medical University Eye Hospital（天津医科大学眼科医院）；Capital Medical University（首都医科大学）；Beijing Tongren Hospital（北京同仁医院）
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…
Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparam…
University of Nevada, Reno（内华达大学里诺分校）；Deakin University（迪肯大学）；Houston Eye Associates（休斯顿眼科协会）；University of Nevada School of Medicine（内华达大学医学院）
Carrying out clinical diagnosis of retinal vascular degeneration using Fluorescein Angiography (FA) is a time consuming process and can pose significant adverse effects on the patient. Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal. Currently, there are no non-invasive systems capable of generating Fluorescein Angiography images. However, retinal fundus photography is a non-invasive imaging technique that can be completed in a few seconds. In order to eliminate the need for FA, we propose a conditional generative adversarial network (GAN) to…
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for th…
The appearance and structure of blood vessels in retinal images have an important role in diagnosis of diseases. This paper proposes a method for automatic retinal vessel segmentation. In this work, a novel preprocessing based on local histogram equalization is used to enhance the original image then pixels are classified as vessel and non-vessel using a classifier. For this classification, special feature vectors are organized based on responses to Morlet wavelet. Morlet wavelet is a continues transform which has the ability to filter existing noises after preprocessing. Bayesian classifier i…
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（健康研究所（IdISSC））；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not viable, since the population at-risk is large and the analysis must be performed by expert clinicians. Also, the diagnosis of AMD is considered to be particularly difficult, as it is characterized by many different lesions that, in many case…
Beijing Institute of Technology（北京理工大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Hospital（北京同仁医院）
Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the…
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）；Opthalmology Clinic（眼科诊所）；National University of Science and Technology (NUST)（国立科技大学）；National Center of Artificial Intelligence（国家人工智能中心）；Smart Data and Knowledge Services（智能数据与知识服务部）
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retin…
David R. Cheriton School of Computer Science（大卫·R·切里顿计算机科学学院）；University of Waterloo（滑铁卢大学）
Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimedia image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. The early detection of many such conditions is often the most effective method for reducing patient risk. Computer ai…
Rochester Institute of Technology（罗切斯特理工学院）；Drexel University（德雷塞尔大学）；University of Rochester（罗切斯特大学）
Widespread outreach programs using remote retinal imaging have proven to decrease the risk from diabetic retinopathy, the leading cause of blindness in the US. However, this process still requires manual verification of image quality and grading of images for level of disease by a trained human grader and will continue to be limited by the lack of such scarce resources. Computer-aided diagnosis of retinal images have recently gained increasing attention in the machine learning community. In this paper, we introduce a set of neural networks for diabetic retinopathy classification of fundus reti…
Eindhoven University of Technology（埃因霍温理工大学）；Northeastern University（东北大学）
Retinal microaneurysms are the earliest clinical sign of diabetic retinopathy disease. Detection of microaneurysms is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of microaneurysms in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on loca…
SD-OCT is a non-invasive cross-sectional imaging modality used for diagnosis of macular defects. Efficient detection and segmentation of the abnormalities seen as biomarkers in OCT can help in analyzing the progression of the disease and advising effective treatment for the associated disease. In this work, we propose a fully automated Generalized Motion Pattern(GMP) based segmentation method using a cascade of fully convolutional networks for detection and segmentation of retinal fluids from SD-OCT scans. General methods for segmentation depend on domain knowledge-based feature extraction, wh…
CITIC-Research Center of Information and Communication Technologies, University of A Coruña（科蒂克信息与通信技术研究中心，拉科鲁尼亚大学）；Department of Computer Science, University of A Coruña（拉科鲁尼亚大学计算机科学系）；University of A Coruña（拉科鲁尼亚大学）；CITIC-Research Center of Information and Communication Technologies（科蒂克信息与通信技术研究中心）
The analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and follow-up of relevant diseases, like hypertension or diabetes. This work presents a hybrid method for the multimodal registration of color fundus retinography and fluorescein angiography. The proposed method combines a feature-based approach, using domain-specific landmarks, with an intensity-based approach that employs a domain-adapted similarity metric. The methodology is tested on a dataset of 59 image pairs containing both healthy and pathologic…
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
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）；School of Computing, Clemson University（克莱姆森大学计算机学院）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；The Keck School of Medicine of the University of Southern California（南加州大学凯克医学院）；Mayo Clinic（梅奥诊所）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）
Universidad Nacional de Colombia（哥伦比亚国立大学）；Fundación Oftalmológica Nacional（国家眼科基金会）；Universidad del Rosario（罗萨里奥大学）
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, provid…
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy
Amirkabir University of Technology（阿米尔卡比尔理工大学）；Tehran Medical Sciences, Islamic Azad University（伊斯兰阿扎德大学德黑兰医学科学分校）
University of Waterloo（滑铁卢大学）；Medical Research Foundation（医学研究基金会）；Sankara Nethralaya（桑卡拉·内特拉拉亚眼科医院）
Background: The lack of explanations for the decisions made by algorithms such as deep learning has hampered their acceptance by the clinical community despite highly accurate results on multiple problems. Recently, attribution methods have emerged for explaining deep learning models, and they have been tested on medical imaging problems. The performance of attribution methods is compared on standard machine learning datasets and not on medical images. In this study, we perform a comparative analysis to determine the most suitable explainability method for retinal OCT diagnosis. Methods: A com…
Early Alzheimer's Disease Detection from Retinal OCT Images: A UK Biobank Study
Department of Computer Engineering Isik University Istanbul, Turkey（计算机工程系伊斯肯大学）；Department of Artificial Intelligence and Data Engineering（人工智能与数据工程系）；Data Engineering Istanbul Technical University Istanbul, Turkey（数据工程系伊斯坦布尔技术大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Oregon（俄勒冈大学）
Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-performance deep learning-based approaches to segment the retinal images in an end-to-end manner. However, the acquisition of retinal vessel images and segmentation labels requires onerous work from professional clinicians, which results in smaller training dataset with incomplete labels. As known, data-driven methods suffer from data insufficiency, and the models will easily over-fit the small-scale training data. Such a s…
An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
Nanotechnology, Biotechnology, Information Technology and Cognitive Science Laboratory, University of Tehran（特拉布宗大学纳米技术、生物技术、信息技术和认知科学实验室）；Department of Mechatronics, School of Intelligent Systems, College of Interdisciplinary Science and Technology, University of Tehran（特拉布宗大学机电系，智能系统学院，跨学科科学与技术学院）；School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England（新英格兰大学科学与技术学院，科学、农业、商业与法律学院）；Graduate School of Health, University of Technology Sydney（悉尼技术大学健康研究生院）；School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle（新castle大学医学与公共卫生学院，健康、医学与福祉学院）
