University of Oxford（牛津大学）；CRO Centro Riferimento Oncologico IRCCS Aviano（阿维亚诺肿瘤参考中心IRCCS）；University of Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；University of Lincoln（林肯大学）
Sharib Ali, Noha Ghatwary, Barbara Braden, Dominique Lamarque, Adam Bailey, Stefano Realdon, Renato Cannizzaro, Jens Rittscher, Christian Daul, James East
Whilst many technologies are built around endoscopy, there is a need to have a comprehensive dataset collected from multiple centers to address the generalization issues with most deep learning frameworks. What could be more important than disease detection and localization? Through our extensive network of clinical and computational experts, we have collected, curated and annotated gastrointestinal endoscopy video frames. We have released this dataset and have launched disease detection and segmentation challenge EDD2020 https://edd2020.grand-challenge.org to address the limitations and explore new directions. EDD2020 is a crowd sourcing initiative to test the feasibility of recent deep learning methods and to promote research for building robust technologies. In this paper, we provide an overview of the EDD2020 dataset, challenge tasks, evaluation strategies and a short summary of results on test data. A detailed paper will be drafted after the challenge workshop with more detailed analysis of the results.
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Northwestern University（西北大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Jefferson Health NJ（新泽西杰斐逊医疗）；SimulaMet（西穆拉梅特研究所）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
University of Oxford（牛津大学）；Ludwig Institute for Cancer Research（路德维希癌症研究所）；University of Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；John Radcliffe Hospital（约翰·拉德克利夫医院）；Swiss Federal Institute of Technology at Lausanne (EPFL)（洛桑联邦理工学院）；University of Padova（帕多瓦大学）
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realizing reliable computer-assisted tools for improved patient care. At present most videos in endoscopy are currently not analyzed due to the abundant presence of multi-class artifacts in video frames. Through the endoscopic artifact detection (EAD 2019) challenge, we address this key bottleneck …
Federated Foundation Model for GI Endoscopy Images
Lane Department of Computer Science and Electrical Engineering, West Virginia University（沃里奇大学计算机科学与电气工程系）；West Virginia University（沃里奇大学）；School of Medicine, West Virginia University（沃里奇大学医学院）；University of Memphis（密苏里州孟菲斯大学）；University of Aberdeen（阿伯丁大学）
SimulaMet；UIT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Simula Research Laboratory（Simula研究实验室）；Oslo Metropolitan University（奥斯陆城市大学）；Sintef Digital（SINTEF数字研究所）；Augere Medical AS（Augere Medical公司）；Sahlgrenska University Hospital-Mölndal（萨尔格伦斯卡大学医院-默恩达尔）；Bærum Hospital（贝鲁姆医院）；Karolinska University Hospital（卡罗林斯卡大学医院）
Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the ``Kvasir-Instrument'' dataset which consists of $590$ annotated frames cont…
Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation of the large intestine. The severity of UC is often represented by the Mayo Endoscopic Subscore (MES) which quantifies mucosal disease activity from endoscopy videos. In clinical trials, an endoscopy video is assigned an MES based upon the most severe disease activity observed in the video. For this reason, severe inflammation spread throughout the colon will receive the same MES as an otherwise healthy colon with severe inflammation restricted to a small, localized segment. Therefore, the e…
PDPM Indian Institute of Information Technology, Design and Manufacturing（PDPM印度信息技术设计与制造学院）
Wireless Capsule Endoscopy is one of the most advanced non-invasive methods for the examination of gastrointestinal tracts. An intelligent computer-aided diagnostic system for detecting gastrointestinal abnormalities like polyp, bleeding, inflammation, etc. is highly exigent in wireless capsule endoscopy image analysis. Abnormalities greatly differ in their shape, size, color, and texture, and some appear to be visually similar to normal regions. This poses a challenge in designing a binary classifier due to intra-class variations. In this study, a hybrid convolutional neural network is propos…
Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X
Indian Institute of Technology, Indore (IITI)（印度理工学院印多尔分校）；Thapar Institute of Engineering & Technology (TIET)（塔帕尔工程技术学院）；Vellore Institute of Technology, Chennai (VIT)（韦洛尔理工学院 Chennai 校区）
Endoscopy provides a major contribution to the diagnosis of the Gastrointestinal Tract (GIT) diseases. With Colon Endoscopy having its certain limitations, Wireless Capsule Endoscopy is gradually taking over it in the terms of ease and efficiency. WCE is performed with a miniature optical endoscope which is swallowed by the patient and transmits colour images wirelessly during its journey through the GIT, inside the body of the patient. These images are used to implement an effective and computationally efficient approach which aims to detect the abnormal and normal tissues in the GIT automati…
A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
Department of Computer Science and Engineering（计算机科学与工程系）
Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures and transmits images to an external receiver. WCE devices are considered as a replacement of conventional endoscopy methods which are usually painful and distressful for the patients. WCE devices produce over 60,000 images typically during their course of operation inside the GI tract. These images need to be examined by expert physicians who attempt to identify frames that contain inflammation / disease. It can be hectic f…
ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors
Department of Biomedical Informatics, Harvard Medical School（生物医学信息学系，哈佛医学院）；Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center（先进内窥镜中心，贝塞斯达医院）
University of Oxford（牛津大学）；Bogazici University（博阿齐奇大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Friedrich Alexander Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Harvard Medical School（哈佛医学院）
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics. Enhanced-resolution endoscopy has shown to improve adenoma detection rate for conventional endoscopy and is likely to do the same for capsule endoscopy. In this work, we propose and quantitatively validate a novel framework to learn a mapping from low-to-high resolution endoscopic images. We combine conditional adversarial networks with a spatial attention block to improve the re…
Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple ru…
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new ave…
SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment
National Research Council of Italy (CNR), Institute for High-Performance Computing and Networking (ICAR), Naples（意大利国家研究理事会（CNR）、高性能计算与网络研究所（ICAR）、那不勒斯）
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
The University of Sydney（悉尼大学）；The Chinese University of Hong Kong (CUHK)（香港中文大学）；Northeastern University（东北大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
University of Minnesota（明尼苏达大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medicine（威尔康奈尔医学院）；Mayo Clinic（梅奥诊所）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Yale University（耶鲁大学）；University of Pennsylvania（宾夕法尼亚大学）；Arizona State University（亚利桑那州立大学）；The University of Tokyo Hospital（东京大学医院）；Carnegie Mellon University（卡内基梅隆大学）；Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)（千禧年智能医疗工程研究所）；National Center for Artificial Intelligence (CENIA)（国家人工智能中心）；Xiamen University（厦门大学）；Seoul National University（首尔大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究院）；Thomas Jefferson University Hospital（托马斯杰斐逊大学医院）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；National Institutes of Health（美国国立卫生研究院）；National Library of Medicine（美国国家医学图书馆）
本文介绍 MICCAI 挑战赛 CXR-LT 2024，扩展大规模长尾胸部 X 光数据集并设置三项分类任务，汇总多模态、生成式与零样本方法以提升临床诊断泛化能力。
Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025
Department of Biomedical Informatics, Emory University（埃默里大学生物医学信息学系）；Department of Electronic Technology, University of the Basque Country UPV/EHU（巴斯克国家大学UPV/EHU电子技术系）；Department of Information Technology, Uppsala University（乌普萨拉大学信息科技系）；Telehealth Center from Hospital das Clínicas, Universidade Federal de Minas Gerais（弗拉门戈联邦大学医院诊所的远程医疗中心）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Emory University（埃默里大学）
2025年George B. Moody PhysioNet挑战赛围绕基于心电图检测恰加斯病的任务，通过提供多标签数据集、数据增强、适配检测能力的分诊评估指标推动研究，吸引全球111支队伍630余人参与，提交了1300余份方案。
Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases
China Three Gorges University（三峡大学）；College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；University of Birmingham（伯明翰大学）；Southern University of Science and Technology（南方科技大学）
Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning
Jeonbuk National University（全北国立大学）；Tianjin University of Science and Technology（天津科技大学）；Mokpo National University（木浦大学）；National Institute of Agricultural Sciences（国立农业科学研究所）
Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance on various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets for training and testing algorithms. Then, we…
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, 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…
The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time …
