For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into use because of its broader insights on fundus for some lesions that are not typically seen in traditional fundus images. Research on traditional fundus images is an active topic but studies on UWF fundus images are few. One of the most important reasons is that UWF fundus images are hard to obtain. In this paper, for the first time, we explore domain adaptation from the traditional fundus to UWF fundus images. We propose a flexible framework to bridge the domain gap between two domains and co-train a UWF fundus diagnosis model by pseudo-labelling and adversarial learning. We design a regularisation technique to regulate the domain adaptation. Also, we apply MixUp to overcome the over-fitting issue from incorrect generated pseudo-labels. Our experimental results on either single or both domains demonstrate that the proposed method can well adapt and transfer the knowledge from traditional fundus images to UWF fundus images and improve the performance of retinal disease recognition.
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Southwest University（西南大学）；Taiyuan University（太原学院）；Taiyuan University of Technology（太原理工大学）；Shanxi Eye Hospital（山西省眼科医院）；Nanjing University of Information Science and Technology（南京信息工程大学）
Fundus image enhancement through direct diffusion bridges
Kim Jae Chul Graduate School of AI, KAIST（KAIST 金在哲人工智能研究生院）；Bio & Brain Engineering, KAIST（KAIST 生物与脑工程系）；Kangnam Sacred Heart Hospital, Hallym University School of Medicine（翰林大学医学院江南圣心医院）；SNU Seoul Eye Clinic（首尔大学首尔眼科诊所）
This study is to test the feasibility of using trans-pars-planar illumination for ultrawide field pediatric fundus photography. Fundus examination of the peripheral retina is essential for clinical management of pediatric eye diseases. However, current pediatric fundus cameras with traditional trans-pupillary illumination provide a limited field of view (FOV), making it difficult to access the peripheral retina adequately for a comprehensive assessment of eye conditions. Here, we report the first demonstration of trans-pars-planar illumination in ultra-wide field pediatric fundus photography. …
Monash University（莫纳什大学）；Airdoc；NVIDIA AI Technology Center（英伟达人工智能技术中心）
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based retinal vessel segmentation algorithms have achieved remarkable performance. However, due to the domain shift problem, the performance of these algorithms often degrades when they are applied to new data that is different from the training data. Manually labeling new data for each test domain is often a time-consuming and laborious task. In this work, we explore unsupervised domain adaptation in retinal vessel segmen…
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…
University of Rochester（罗切斯特大学）；University of Rochester Medical Center（罗切斯特大学医学中心）；Houston Methodist Hospital（休斯顿卫理公会医院）；Weill Cornell Medical College（威尔康奈尔医学院）；Retina Consultants of Houston（休斯顿视网膜顾问中心）；Blanton Eye Institute（布兰顿眼科研究所）
While recent advances in deep learning have significantly advanced the state of the art for vessel detection in color fundus (CF) images, the success for detecting vessels in fluorescein angiography (FA) has been stymied due to the lack of labeled ground truth datasets. We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured CF and fundus…
UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement
Hangzhou Dianzi University（杭州电子科技大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen University（深圳大学）；Misr Higher Institute for Commerce and Computers（米斯尔高等商务与计算机学院）
Universidad de Sevilla（塞维利亚大学）；Universidad de Cádiz（加的斯大学）；School of Engineering, Universidad de Cádiz（加的斯大学工程学院）
Medical image segmentation can be implemented using Deep Learning methods with fast and efficient segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their memory and processing limitations. Specific hardware such as Google's Edge TPU makes them suitable for real time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be applied to the segmentation by deep neural networks of other type…
Ultrasound-CLIP: Semantic-Aware Contrastive Pre-training for Ultrasound Image-Text Understanding
Hangzhou City University（杭州城市大学）；Hong Kong Baptist University（香港浸会大学）；Zhejiang University（浙江大学）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）；City University of Hong Kong（香港城市大学）
UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image
Academy of Advanced Interdisciplinary Studies, Wuhan University（武汉大学高等研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Guangzhou Women and Children’s Medical Center（广州市妇女儿童医疗中心）；Institute of Artificial Intelligence, School of Computer Science, Wuhan University（武汉大学计算机学院人工智能研究所）
Fudan University（复旦大学）；Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海市第一人民医院）
The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the image quality assessment task, we create an ensemble of Inception…
OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
School of Data Science, Fudan University（复旦大学数据科学学院）；The Hong Kong Polytechnic University（香港理工大学）；Eye & ENT Hospital, Fudan University（复旦大学眼耳鼻喉科医院）；College of Business and Economics, Australian National University（澳大利亚国立大学商学与经济学院）
