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）
Yan Luo, Yu Tian, Min Shi, Louis R. Pasquale, Lucy Q. Shen, Nazlee Zebardast, Tobias Elze, Mengyu Wang
Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}.
Technical University of Munich（慕尼黑工业大学）；Heritage Institute of Technology（印度传统理工学院）
Automatic evaluation of the retinal fundus image is emerging as one of the most important tools for early detection and treatment of progressive eye diseases like Glaucoma. Glaucoma results to a progressive degeneration of vision and is characterized by the deformation of the shape of optic cup and the degeneration of the blood vessels resulting in the formation of a notch along the neuroretinal rim. In this paper, we propose a deep learning-based pipeline for automatic segmentation of optic disc (OD) and optic cup (OC) regions from Digital Fundus Images (DFIs), thereby extracting distinct fea…
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；National University of Singapore（新加坡国立大学）；Aravind Eye Care Systems（阿拉温德眼科护理系统）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma, and monitoring these changes could be critical for improved diagnosis and prognosis in the glaucoma clinic. The gold-standard technique to assess structural changes of the ONH clinically is optical coherence tomography (OCT). However, OCT is limited to the measurement of a few hand-engineered parameters, such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet been qualified as a stand-alone device for glaucoma diagnos…
Google Health（谷歌健康）；Google LLC（谷歌有限责任公司）；Virginia Ophthalmology Associates（弗吉尼亚眼科协会）；Eastern Virginia Medical School（东弗吉尼亚医学院）；Duke University（杜克大学）；Emory University School of Medicine（埃默里大学医学院）；Atlanta Veterans Affairs Medical Center（亚特兰大退伍军人事务医疗中心）；Dr. Shroff’s Charity Eye Hospital（施罗夫博士慈善眼科医院）
Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand screening access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning algorithm using a retrospective dataset of 86,618 images, assessed for glaucomatous optic nerve head features and referable glaucomatous optic neuropathy …
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；National University of Singapore（新加坡国立大学）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Vilnius University（维尔纽斯大学）；Vilnius University Hospital Santaros Klinikos（维尔纽斯大学圣罗克利什科斯医院）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is the current gold standard to visualize and quantify these changes, however the resulting 3D deep-tissue information has not yet been fully exploited for the diagnosis and prognosis of glaucoma. To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ON…
3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工研究与技术联盟）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Emory University School of Medicine（埃默里大学医学院）；National University of Singapore（新加坡国立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory Empathetic AI for Health Institute（埃默里大学共情健康人工智能研究所）；Emory Eye Center（埃默里眼科中心）；Emory School of Medicine（埃默里医学院）；Emory Clinic（埃默里诊所）；Abyss Processing Pte Ltd（Abyss Processing私人有限公司）
Explainable Severity ranking via pairwise n-hidden comparison: a case study of glaucoma
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；College of Engineering and Computer Science, Vin University（文大学工程与计算机科学学院）
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Nanyang Technological University Singapore（新加坡南洋理工大学）；Medical University of Vienna（维也纳医科大学）；Institute of Molecular and Clinical Ophthalmology, Basel（巴塞尔分子与临床眼科研究所）；Abyss Processing Pte Ltd（深渊处理私人有限公司）
Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab…
Baidu Inc.（百度公司）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma.OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures. To promote the research of AI technology in the field of OCT-assisted diagnosis of glaucoma, we held a Glaucoma OCT Analysis and Layer Segmentation (GOALS) Challenge in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 to provide data and corresponding annotations for researchers studying …
Glaucoma is a disease in which the optic nerve is chronically damaged by the elevation of the intra-ocular pressure, resulting in visual field defect. Therefore, it is important to monitor and treat suspected patients before they are confirmed with glaucoma. In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma. Furthermore, we propose a method of using the class activation map as a mask filter and combining it with the original fundus image as an intermediate input. Our results have improved the average accuracy by about 10% over the…
GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining
Department of Computer Science, Southern Methodist University（计算机科学系，南方 Methodist 大学）；Department of Ophthalmology, University of Texas Southwestern Medical Center（眼科系，德克萨斯南方医学中心大学）；National Aeronautics and Space Administration（美国国家航空航天局）；Department of Pharmacy and Health Systems Sciences, Northeastern University（药学与健康系统科学系，东北大学）
AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification
Department of Computer Science, Columbia University, New York, NY, USA（计算机科学系，哥伦比亚大学）；Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA（眼科学系，哥伦比亚大学伊万杰琳医疗中心）；Department of Biomedical Engineering, Columbia University, New York, NY, USA（生物医学工程系，哥伦比亚大学）；The Data Science Institute, Columbia University, New York, NY, USA（数据科学学院，哥伦比亚大学）
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…
Matroid（Matroid 公司）；University of Maryland at Baltimore County（马里兰大学巴尔的摩县分校）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；The Chinese University of Hong Kong（香港中文大学）；Tilganga Institute of Ophthalmology（提尔甘加眼科研究所）；Narayana Nethralaya（纳拉亚纳眼科医院）；Stanford University（斯坦福大学）
We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Using this longitudinal data, we achieved state-of-the-art results for automatically detecting Glaucoma from a single raw OCT using a 3D Deep Learning system. These results are close to human doctors in a variety of settings across heterogeneous datasets an…
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Lancaster University（兰卡斯特大学）；Aravind Eye Hospital（阿拉温德眼科医院）；Slovak University of Technology（斯洛伐克理工大学）；Ulster University（阿尔斯特大学）
Glaucoma is a leading cause of irreversible vision impairment globally and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention which can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the centre of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is can artificial intelligence mimic gla…
National University of Sciences and Technology（国立科技大学）；Khalifa University（哈利法大学）
Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with …
Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa
Emory University（埃默里大学）；National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立眼科中心新加坡眼科研究所）；University of Ibadan（伊巴丹大学）；Duke University School of Medicine（杜克大学医学院）；Duke-NUS Graduate Medical School-Singapore（新加坡杜克-新加坡国立大学研究生医学院）；Georgia Institute of Technology（佐治亚理工学院）；Emory Empathetic AI for Health Institute, Emory University（埃默里大学埃默里共情健康人工智能研究所）；Abyss Processing Pte Ltd（Abyss Processing私人有限公司）
Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼科研究所，新加坡国家眼科中心）；National University of Singapore（新加坡国立大学）；Vilnius University（维尔纽斯大学）；Vilnius University Hospital Santaros Klinikos（维尔纽斯大学桑塔罗斯诊所医院）；Aravind Eye Care Systems（阿拉文眼科医疗系统）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）
Purpose: To assess whether the three-dimensional (3D) structural configuration of the central retinal vessel trunk and its branches (CRVT&B) could be used as a diagnostic marker for glaucoma. Method: We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomography (OCT) volume of the optic nerve head (ONH). Subsequently, two different approaches were used for glaucoma diagnosis using the structural configuration of the CRVT&B as extracted from the OCT volumes. In the first approach, we aimed to provide a diagnosis using only 3D CNN and…
Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Images
Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Eye Center（北京同仁眼科中心）；Beijing Tongren Hospital（北京同仁医院）；Capital Medical University（首都医科大学）；Beijing Key Laboratory of Ophthalmology and Visual Sciences（北京市眼科与视觉科学重点实验室）
Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the r…
Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle Closure Glaucoma Progression
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Emory University School of Medicine（埃默里大学医学院）；Emory University（埃默里大学）；National University of Singapore（新加坡国立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory Empathetic AI for Health Institute（埃默里健康共情人工智能研究所）
Schepens Eye Research Institute of Massachusetts Eye and Ear（麻省眼耳医院谢本斯眼科研究所）；Harvard Medical School（哈佛医学院）；Eye and Vision Research Institute, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院眼与视觉研究所）；Massachusetts Eye and Ear（麻省眼耳医院）
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e.g., glaucoma). For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) linked to functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. The ta…
Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore（新加坡国立大学工程学院生物医学工程系眼科工程与创新实验室）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立眼科中心新加坡眼科研究所）；Rajan Eye Care Hospital（拉詹眼科护理医院）
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, t…
Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Biomedical Data Analysis and Visualisation (BDAV) Lab, School of Computer Science（生物医学数据分析与可视化实验室，计算机科学学院）；Sydney School of Public Health（悉尼公共卫生学院）；School of Computing（计算学院）
