The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help physicians with the video screening and, finally, with the diagnosis. Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, new CAD system has to be designed from scratch. This characteristic makes the design of new CAD systems a very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which avoids the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed by using state of the art hand-crafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).
Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy
Danube Private University（多瑙河私立大学）；Indira Gandhi Delhi Technical University for Women（英迪拉·甘地德里女子技术大学）；Guru Gobind Singh Indraprastha University（古鲁·戈宾德·辛格·因德拉普拉斯塔大学）；Delhi Technological University（德里理工大学）；Columbia University（哥伦比亚大学）；All India Institute of Medical Sciences（全印度医学科学研究所）；Indian Institute of Technology Roorkee（鲁基印度理工学院）
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；University of Science and Technology Beijing（北京科技大学）；City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）
EndoOOD: Uncertainty-aware Out-of-distribution Detection in Capsule Endoscopy Diagnosis
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）
CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis
School of Computer Science, AI and Electronics Faculty of Engineering and Digital Technologies（计算机科学与电子工程学院，工程与数字技术学院）；School of Management Faculty of Mgmt, Law & Social Sciences（管理学院，管理、法律与社会科学学院）；Centre for Visual Computing and Intelligent Systems（视觉计算与智能系统中心）
Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies
Department of Computer Science, University of Tübingen（图宾根大学计算机科学系）；Seminar of Applied Mathematics, ETH Zürich（苏黎世联邦理工学院应用数学研讨班）；Else Kröner Fresenius Center for Digital Health, TU Dresden（德累斯顿技术大学数字健康研究中心）；Department of Medicine I, University Hospital Dresden, TU Dresden（德累斯顿大学医院第一医学部，德累斯顿技术大学）
CapsoNet: A CNN-Transformer Ensemble for Multi-Class Abnormality Detection in Video Capsule Endoscopy
Department of Computer Science and Engineering, National Institute of Technology Rourkela（计算机科学与工程系，印度国立技术学院鲁尔克ela分校）；Department of Artificial Intelligence and Data Sciences, Indira Gandhi Delhi Technical University for Women（人工智能与数据科学系，印度甘地德里技术女子大学）
Using Multi-Instance Learning to Identify Unique Polyps in Colon Capsule Endoscopy Images
Department of Automation and Process Engineering, Machine Learning Group（自动化与过程工程系，机器学习组）；UiT – The Arctic University of Norway（UiT – 北极大学）；Department of Physics and Technology, Machine Learning Group（物理与技术系，机器学习组）；University of Waikato（瓦卡托大学）；Odense University Hospital（奥登塞大学医院）；University of Southern Denmark（南丹麦大学）
Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education（教育部智慧医疗技术工程研究中心）；School of C（C学院）
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
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）
GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results
School of Computer Science and Engineering, Kyungpook National University（韩国庆北国立大学计算机科学与工程学院）
Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet-B7 for Improved Gastrointestinal Abnormality Classification in Video Capsule Endoscopy
International Institute of Information Technology, Bangalore（国际信息技术学院，班加罗尔）；Indian Institute of Technology, Hyderabad（印度理工学院，海得拉巴）
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
Indian Statistical Institute（印度统计研究所）；Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Feinberg School of Medicine, Northwestern University（西北大学范伯格医学院）
Seeing More with Less: Video Capsule Endoscopy with Multi-Task Learning
Department of Computer Science, University of Tübingen, Tübingen, Germany（图宾根大学计算机科学系）；Else Kröner Fresenius Center for Digital Health, TU Dresden, Dresden, Germany（德累斯顿技术大学数字健康研究中心）；Department of Medicine I, University Hospital Dresden, TU Dresden, Dresden, Germany（德累斯顿大学医院第一医学系）
A Highlight Removal Method for Capsule Endoscopy Images
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education（教育部智慧医疗技术工程研究中心）
CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy
Netaji Subhas University of Technology（贾瓦哈拉尔·尼赫鲁技术大学）；Helios Tech Solutions Pvt Ltd（赫利奥斯技术解决方案私人有限公司）；Centre for Artificial Intelligence and Machine Learning（人工智能与机器学习中心）
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
SimulaMet；UiT The Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯陆城市大学）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Bærum Hospital（贝鲁姆医院）；Augere Medical AS（Augere Medical 公司）；Sahlgrenska University Hospital-Mölndal Hospital（萨尔格伦斯卡大学医院-默恩达尔医院）；Sahlgrenska Academy, University of Gothenburg（哥德堡大学萨尔格伦斯卡学院）
V$^2$-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy
The Chinese University of Hong Kong（香港中文大学）；City University of Hong Kong（香港城市大学）；Southern University of Science and Technology（南方科技大学）；Hubei University of Technology（湖北工业大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nantong University（南通大学）
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）
VR-Caps: A Virtual Environment for Capsule Endoscopy
Bogazici University（博阿齐奇大学）；University of Oxford（牛津大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Dana Farber Cancer Institute（丹娜-法伯癌症研究所）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院布罗德研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；Louisiana State University（路易斯安那州立大学）
Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations
National Institute of Technology Puducherry（本地治里国家技术学院）；Sardar Vallabhbhai National Institute of Technology Surat（苏拉特萨达尔·瓦拉巴伊国家技术学院）；Norwegian University of Science and Technology（挪威科技大学）
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models
Captos Co., Ltd.（Captos株式会社）；Seoreu Co., Ltd.（Seoreu株式会社）；Pusan National University School of Medicine（釜山大学医学院）；Pusan National University Yangsan Hospital（釜山大学梁山医院）；Kyungpook National University（庆北大学）
