ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection
College of Electronics & Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学与技术研究院）；School of Life Science, Shanghai University（上海大学生命科学学院）；Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院）
Jiayi Wang, Yi-An Mao, Xiaoyu Ma, Sicen Guo, Yuting Shao, Xiao Lv, Wenting Han, Mark Christopher, Linda M. Zangwill, Yanlong Bi, Rui Fan
Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at mias.group/ODFormer.
Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；“Iuliu Hatieganu” University of Medicine and Pharmacy（“尤利乌·哈采加努”医药大学）；Emergency County Hospital（急诊县医院）
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）
Pathological myopia classification with simultaneous lesion segmentation using deep learning
Research Group Ophthalmology, KU Leuven（KU Leuven眼科研究组）；Ophthalmology Department, UZ Leuven（UZ Leuven眼科部）；ESAT-PSI, KU Leuven（KU Leuven ESAT-PSI）；Hasselt University（哈塞尔特大学）；VITO NV
Deep learning on fundus images detects glaucoma beyond the optic disc
KU Leuven（鲁汶大学）；UZ Leuven（鲁汶大学医院）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；Hasselt University（哈瑟尔特大学）；University of Antwerp（安特卫普大学）；Flemish Institute for Technological Research（弗拉芒技术研究院）
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）
