Accepted for publication at SIPAIM 2025. v2: Corrected a labeling bug regarding DME positive class criteria. Experimental results updated after retraining; main conclusions remain unchanged
Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative solution. However, it is unclear if they can cope with DME detection in particular. In this paper, we systematically compare different FM and standard transfer learning approaches for this task. Specifically, we compare the two most popular FM for retinal images-RETFound and FLAIR-and an EfficientNetB0 backbone, across different training regimes and evaluation settings in IDRiD, MESSIDOR-2 and OCT-and-Eye-FundusImages (OEFI). Results show that despite their scale, FM do not consistently outperform fine-tuned CNNs in this task. In particular, EfficientNet-B0 consistently achieves competitive or superior performance across evaluation settings, with FLAIR being the most competitive foundation model, consistently outperforming RETFound. These findings suggest that FMs do not necessarily provide an advantage for fine-grained ophthalmic tasks such as DME detection, even after fine-tuning, highlighting lightweight CNNs as strong baselines in data-scarce environments.
In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected conditional random fields (dense CRFs). As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME regions using the trained model. Thereafter, dense CRFs are used to refine the segmentation according to the edema appearance. We have trained and tested the framework with OCT images from 10 patients wit…
Technion Israel Institute of Technology（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）；University of Adelaide（阿德莱德大学）；Royal Adelaide Hospital（阿德莱德皇家医院）；South Australian Institute of Ophthalmology（南澳大利亚眼科研究所）；Clinical Research Institute at Rambam（兰巴姆临床研究所）
To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).
School of Advanced Technologies in Medicine（医学先进技术学院）；Isfahan University of Medical Sciences（伊斯法罕医科大学）
Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudate are the most significant signs of these diseases. With the aim of automatic classification of DME, AMD and normal subjects from Optical Coherence Tomography (OCT) images, we proposed a classification algorithm. The two important issues intended in this approach are, not utilizing retinal layer segmentation which by itself is a challenging task and attempting to identify diseases in their early stages,…
Aalto University School of Science（阿尔托大学理学院）；Digifundus Ltd.（Digifundus有限公司）；Central Finland Central Hospital（中芬兰中心医院）
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also …
Background: Patients with neovascular age-related macular degeneration (AMD) can avoid vision loss via certain therapy. However, methods to predict the progression to neovascular age-related macular degeneration (nvAMD) are lacking. Purpose: To develop and validate a deep learning (DL) algorithm to predict 1-year progression of eyes with no, early, or intermediate AMD to nvAMD, using color fundus photographs (CFP). Design: Development and validation of a DL algorithm. Methods: We trained a DL algorithm to predict 1-year progression to nvAMD, and used 10-fold cross-validation to evaluate this a…
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）
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false p…
University of California, Berkeley（加利福尼亚大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；University of California, Davis（加利福尼亚大学戴维斯分校）
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-related macular degeneration (AMD) severity using fundus photographs from the Age-Related Eye Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility across different AMD classification schemes without retraining, and achiev…
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（国家科学技术研究委员会）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Inserm（法国国家健康与医学研究院）；Univ Bretagne Occidentale（西布列塔尼大学）；Zasti（Zasti公司）
Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Au…
Technische Universität München（慕尼黑工业大学）；Ludwig-Maximilians-University（路德维希-马克西米利安大学）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；Johns Hopkins University（约翰斯·霍普金斯大学）
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and…
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.
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；School of Medical Science and Technology, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校医学科学与技术学院）；Department of Mathematics, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校数学系）
Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
School of Optometry, The Hong Kong Polytechnic University（香港理工大学光学学院）；Department of Ophthalmology, College of Medicine, Rangsit University（皇家理工大学医学院眼科系）；Asia Pacific Tele-Ophthalmology Society（亚太远程眼科学会）；State Key Laboratory (Ophthalmology), Zhongshan Ophthalmic Center, Sun Yat-Sen University（中山眼科中心，中山大学国家重点实验室（眼科））；Duke University（杜克大学）；Texas A&M University（德克萨斯A&M大学）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Xidian University（西电大学）；Svision Imaging Ltd.（Svision影像有限公司）；Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University（SHARP视觉研究中心（RCSV），香港理工大学）；Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park（眼与视觉研究所以（CEVR），香港科学公园17W）；Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong（香港中文大学眼科及视觉科学系）；Faculty of Engineering, The University of Hong Kong（香港大学工程学院）；Faculty of Engineering, The Hong Kong Polytechnic University（香港理工大学工程学院）；Faculty of Health and Life Sciences, University of Liverpool, United Kingdom（利物浦大学健康与生命科学学院，英国）；Shenzhen Technology University, China（深圳技术大学，中国）
本文介绍 APTOS 2021 竞赛，利用 2000 名患者的数万张 OCT 图像预测抗 VEGF 治疗 DME 的反应，最佳模型 AUC 达 80.06%，验证了 AI 在个性化治疗中的价值。
In this manuscript, we automate the procedure of grading of diabetic retinopathy and macular edema from fundus images using an ensemble of convolutional neural networks. The availability of limited amount of labeled data to perform supervised learning was circumvented by using transfer learning approach. The models in the ensemble were pre-trained on a large dataset comprising natural images and were later fine-tuned with the limited data for the task of choice. For an image, the ensemble of classifiers generate multiple predictions, and a max-voting based approach was utilized to attain the f…
Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images
Division of Engineering Science（工程科学系）；University of Toronto（多伦多大学）；School of Information Technology（信息科技学院）；King Mongkut’s University of Technology Thonburi（泰国科技泰伦比大学）；Innovative Cognitive Computing Research Center（创新认知计算研究中心）；Chinese International School of Hong Kong（香港中文国际学校）
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）
Interpretability of deep learning (DL) systems is gaining attention in medical imaging to increase experts' trust in the obtained predictions and facilitate their integration in clinical settings. We propose a deep visualization method to generate interpretability of DL classification tasks in medical imaging by means of visual evidence augmentation. The proposed method iteratively unveils abnormalities based on the prediction of a classifier trained only with image-level labels. For each image, initial visual evidence of the prediction is extracted with a given visual attribution technique. T…
Replication studies are essential for validation of new methods, and are crucial to maintain the high standards of scientific publications, and to use the results in practice. We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22). We re-implemented the method since the source code is not available, and we used publicly available data sets. The original study used non-public fundus images from EyePACS and three hospitals in India for training.…
This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find that, contrary to popular belief, neural architectures within this application setting are able to achieve close to human-level performance on unseen test images without requiring large numbers of training examples. Annotating these 3D datasets is difficult, with multiple criteria required. It takes an experienced clinician two days to annotate a si…
Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for computer aided diagnosis of AMD. We leverage the recent state of the art deep networks for building a single fundus image based AMD classification pipeline. We also propose methods for the other directly relevant and auxiliary tasks such as lesions detection and segmentation, fovea detection and optic disc segmentation. We propose the use of generative adversarial networks (GANs) for …
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 Tehran（德黑兰大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Research Institute for Ophthalmology and Vision Science（眼科与视觉科学研究所）
For medical diagnosis based on retinal images, a clear understanding of 3D structure is often required but due to the 2D nature of images captured, we cannot infer that information. However, by utilizing 3D reconstruction methods, we can recover the height information of the macula area on a fundus image which can be helpful for diagnosis and screening of macular disorders. Recent approaches have used shading information for heightmap prediction but their output was not accurate since they ignored the dependency between nearby pixels and only utilized shading information. Additionally, other m…
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-process…
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）
Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practice, for which the fovea and optic disc (OD) are important retinal landmarks. However, the occurrence of lesions, drusen, and other retinal abnormalities during retinal degeneration severely complicates automatic landmark detection and segmentation. Here we propose HBA-U-Net: a U-Net backbone enriched with hierarchical bottleneck attention. The network consists of a novel bottle…
Medical University Vienna（维也纳医科大学）；Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University Vienna, Austria（维也纳医科大学生物医学成像与影像引导治疗系计算成像研究实验室）；Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系眼科图像分析基督教多普勒实验室）；Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系）
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SD-OCT scans. Evaluation on annotated OCT images o…
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
Ahsanullah University of Science and Technology（阿萨努拉科技大学）
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）
This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we…
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop aut…
Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema
BiometricsAI, Universidad Autónoma de Madrid, Madrid, Spain（生物度量AI，马德里自治大学，马德里，西班牙）；Department of Mathematics, Universidad de Las Palmas de Gran Canaria, Spain（数学系，拉斯帕尔马斯大Canaria大学，西班牙）；HCTLab Research Group, Universidad Autónoma de Madrid, Madrid, Spain（HCTLab研究组，马德里自治大学，马德里，西班牙）
National Center for Biotechnology Information (NCBI)（美国国家生物技术信息中心）；National Library of Medicine (NLM)（美国国家医学图书馆）；National Institutes of Health (NIH)（美国国立卫生研究院）；National Eye Institute (NEI)（美国国家眼科研究所）
In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. DeepSeeNet, a deep learning model, was developed to c…
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）；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（圣卡洛斯健康研究所）；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, it is the most frequent cause of blindness in developed countries. Although some promising treatments have been developed, their effectiveness is low in advanced stages. This emphasizes the importance of large-scale screening programs. Nevertheless, implementing such programs for AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. All this motivates the development of automatic methods. In this sense, severa…
Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. Clinical practitioners use all available data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between all relevant data over a treatment period. Existing datasets…
Kaunas University of Technology（考纳斯理工大学）；Lithuanian University of Health Sciences（立陶宛健康科学大学）
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images. For this, we align invasively obtained eye fundus contrast images (the "golden standard" diagnostic) to the RGB ones and use them to hand-annotate the lesions. This is done using our custom-made tool. Using the data, we train and test five different convolutional neural networks: a custom one to classify healthy and AMD-affected eye fundi, and four well-known networks: ResNet50, ResNet101, MobileNetV3, and UNet to segment (localize) the AMD lesions in the affect…
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）
This paper presents a novel automated system that segments six sub-retinal layers from optical coherence tomography (OCT) image stacks of healthy patients and patients with diabetic macular edema (DME). First, each image in the OCT stack is denoised using a Wiener deconvolution algorithm that estimates the additive speckle noise variance using a novel Fourier-domain based structural error. This denoising method enhances the image SNR by an average of 12dB. Next, the denoised images are subjected to an iterative multi-resolution high-pass filtering algorithm that detects seven sub-retinal surfa…
Medical University Vienna（维也纳医科大学）；University of Vienna（维也纳大学）；NVIDIA Corporation（英伟达公司）
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation. Anomalous regions can then serve as candidates for biomarker discovery. Knowledge about normal anatomical structure br…
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…
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…
Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens. The advent of the retinal OCT has transformed ophthalmology and it is currently paramount for the diagnosis, monitoring and treatment of many eye pathogens including Macular Edema which impairs vision severely or Glaucoma that can cause irreversible blindness. However the quality of retinal OCT images varies among device manufacturers. Deep Learning methods have had their success i…
This paper experiments with the number of fully-connected layers in a deep convolutional neural network as applied to the classification of fundus retinal images. The images analysed corresponded to the ODIR 2019 (Peking University International Competition on Ocular Disease Intelligent Recognition) [9], which included images of various eye diseases (cataract, glaucoma, myopia, diabetic retinopathy, age-related macular degeneration (AMD), hypertension) as well as normal cases. This work focused on the classification of Normal, Cataract, AMD and Myopia. The feature extraction (convolutional) pa…
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）
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院（NIH）国家医学图书馆（NLM）国家生物技术信息中心（NCBI））；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院（NIH）国家眼科研究所（NEI））
Age-related Macular Degeneration (AMD) is a leading cause of blindness. Although the Age-Related Eye Disease Study group previously developed a 9-step AMD severity scale for manual classification of AMD severity from color fundus images, manual grading of images is time-consuming and expensive. Built on our previous work DeepSeeNet, we developed a novel deep learning model for automated classification of images into the 9-step scale. Instead of predicting the 9-step score directly, our approach simulates the reading center grading process. It first detects four AMD characteristics (drusen area…
Radboud University Medical Center（拉德堡德大学医学中心）；Donders Institute for Brain, Cognition and Behaviour（唐德斯脑、认知与行为研究所）；University Hospital Ramón y Cajal（拉蒙·卡哈尔大学医院）；Ramón y Cajal Health Research Institute (IRYCIS)（拉蒙·卡哈尔健康研究所（IRYCIS））；Clínica Rementería（雷门特利亚诊所）
Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases. Methods: Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD b…
Faculty of Informatics, Kaunas University of Technology（信息学院，卡斯图技术大学）
Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision impairment in people over the age of 60. This research focuses on semantic segmentation for AMD lesion detection in RGB fundus images, a non-invasive and cost-effective imaging technique. The results of the ADAM challenge - the most comprehensive AMD detection from RGB fundus images research competition and open dataset to date - serve as a benchmark for our evaluation. Taking the U-Net connectivity as a base of our framework, we evaluate and compare several approaches to improve the segmentation model's…
