We propose a novel pathology-sensitive deep learning model (PS-DeVCEM) for frame-level anomaly detection and multi-label classification of different colon diseases in video capsule endoscopy (VCE) data. Our proposed model is capable of coping with the key challenge of colon apparent heterogeneity caused by several types of diseases. Our model is driven by attention-based deep multiple instance learning and is trained end-to-end on weakly labeled data using video labels instead of detailed frame-by-frame annotation. The spatial and temporal features are obtained through ResNet50 and residual Long short-term memory (residual LSTM) blocks, respectively. Additionally, the learned temporal attention module provides the importance of each frame to the final label prediction. Moreover, we developed a self-supervision method to maximize the distance between classes of pathologies. We demonstrate through qualitative and quantitative experiments that our proposed weakly supervised learning model gives superior precision and F1-score reaching, 61.6% and 55.1%, as compared to three state-of-the-art video analysis methods respectively. We also show our model's ability to temporally localize frames with pathologies, without frame annotation information during training. Furthermore, we collected and annotated the first and largest VCE dataset with only video labels. The dataset contains 455 short video segments with 28,304 frames and 14 classes of colorectal diseases and artifacts. Dataset and code supporting this publication will be made available on our home page.
The following research undertakes a historical review of this technology with specific highlighting of its advancement in medical diagnostics as well as the therapeutic functionality of wireless capsule endoscopy. Without restriction to the gastrointestinal tract alone, the review will additionally investigate the developments in the technology of micro-robots guided through the magnetic power and are capable of navigating through multiple forms of air and fluid filled lumina as well as cavities within the body. All these capabilities are of use in the utilization of minimally invasive medicin…
Isfahan University of Technology（伊斯法罕理工大学）；University of Queensland（昆士兰大学）；University of Michigan（密歇根大学）
Recent advances in capsule endoscopy systems have introduced new methods and capabilities. The capsule endoscopy system, by observing the entire digestive tract, has significantly improved diagnosing gastrointestinal disorders and diseases. The system has challenges such as the need to enhance the quality of the transmitted images, low frame rates of transmission, and battery lifetime that need to be addressed. One of the important parts of a capsule endoscopy system is the image compression unit. Better compression of images increases the frame rate and hence improves the diagnosis process. I…
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…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders. Detection of informative scenes by WCE could reduce the length of transmitted videos and can help with the diagnosis. In this paper we propose a simple and efficient method for segmentation of the bleeding regions in WCE captured images. Suitable color channels are selected and classified by a multi-layer perceptron (MLP) structure. The MLP structure is quantized such that the implementation does not require multiplications. The proposed method is tested by simulation on WCE bleeding image datase…
Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field…
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）
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-worl…
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）
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）
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；University of Michigan（密歇根大学）
Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of gastrointestinal disorders. Detection of informative scenes in WCE video could reduce the length of transmitted videos and help the diagnosis procedure. In this paper, we investigate the problem of simplification of neural networks for automatic bleeding region detection inside capsule endoscopy device. Suitable color channels are selected as neural networks inputs, and image classification is conducted using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) separately. Both CNN and MLP structures are …
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）
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center (CVC)（计算机视觉中心）；Medtronic GI（美敦力胃肠部门）；Hospital Vall d’Hebron（巴尔德希伯伦医院）
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…
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）
Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine the entire GI trace. During an examination, it captures more than 55,000 frames. Reviewing all these images is time-consuming and prone to human error. It has been a challenge to develop intelligent methods assisting physicians to review the frames. The WCE frames are captured in 8-bit color depths which provides enough a color range to detect abnormalities. Here, superpixel based methods are proposed to segment five diseases including: bleeding, Crohn's disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two…
NTNU（挪威科技大学）；SINTEF Digital（挪威科技工业研究院数字分院）；University of Oslo（奥斯陆大学）；Innlandet Hospital Trust（内陆郡医院信托）
The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data. The inadequacy in richly representative healthy and abnormal conditions results in isolated analyses of pathologies, that can not handle realistic multi-pathology scenarios. In this work, we explore how to learn more for free, from limited data through solving a WCE multicentric, multi-pathology classification problem. Learning more implies to learning more than full supervision would allow with the same data. This is done by combining self supervision with full supervision, und…
Wireless Capsule Endoscopy (WCE) is a relatively new technology to record the entire GI trace, in vivo. The large amounts of frames captured during an examination cause difficulties for physicians to review all these frames. The need for reducing the reviewing time using some intelligent methods has been a challenge. Polyps are considered as growing tissues on the surface of intestinal tract not inside of an organ. Most polyps are not cancerous, but if one becomes larger than a centimeter, it can turn into cancer by great chance. The WCE frames provide the early stage possibility for detection…
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）
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…
Universitat de Barcelona（巴塞罗那大学）；CorporateHealth International ApS（CorporateHealth国际有限公司）
State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised …
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）
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Capsule endoscopy is a novel and non-invasive method for diagnosis, which assists gastroenterologists to monitor the digestive track. Although this new technology has many advantages over the conventional endoscopy, there are weaknesses that limits the usage of this technology. Some weaknesses are due to using small-size batteries. Radio transmitter consumes the largest portion of energy; consequently, a simple way to reduce the power consumption is to reduce the data to be transmitted. Many works are proposed to reduce the amount of data to be transmitted consist of specific compression metho…
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）
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians' time for screening, and aid in real-world clinical analysis. CADx classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and …
Effective and rapid detection of lesions in the Gastrointestinal tract is critical to gastroenterologist's response to some life-threatening diseases. Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy procedure by allowing gastroenterologists visualize the entire GI tract non-invasively. Once the tiny capsule is swallowed, it sequentially capture images of the GI tract at about 2 to 6 frames per second (fps). A single video can last up to 8 hours producing between 30,000 to 100,000 images. Automating the detection of frames containing specific lesion in WCE video would …
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）
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）
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to de…
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）
Norwegian University of Science and Technology（挪威科技大学）；Innlandet Hospital Trust（内陆医院信托）
This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV's component V. Enhancement of brighter areas is achie…
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）
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）
Wireless Capsule Endoscopy (WCE) is device to detect abnormalities in colon,esophagus,small intestinal and stomach, to distinguish bleeding in WCE images from non bleeding is a hard job by human reviewing and very time consuming. Consequently, automation for classifying bleeding frames not only will expedite the process but will reduce the burden on the doctors. Using the purity of the red color we can detect the Bleeding areas in WCE images. But, we could find various intensity of red color values in different parts of the small intestinal,so it is not enough to depend on the red color featur…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Coimbra（科英布拉大学）；University Hospital of Coimbra（科英布拉大学医院）
Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy (CCE) is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analys…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Computer Vision Laboratory, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）；University of Coimbra（科英布拉大学）
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI tract and to diagnose various diseases and pathologies. Since the development of this technology, medical device companies and many groups have made significant progress to turn such passive capsule endoscopes into robotic active capsule endoscopes to achieve almost all functions of current active flexible endoscopes. However, the use of robotic capsule endoscopy still has some challenges. One such challenge is the …
Temporal activity localization in long videos is an important problem. The cost of obtaining frame level label for long Wireless Capsule Endoscopy (WCE) videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a non-surgical and non-invasive method to examine the entire digestive tract in order to diagnose diseases or abnormalities. While CE has revolutionized traditional endoscopy procedures, a single CE examination could last up to 8 hours generating as muc…
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）
The article presents a new multi-label comprehensive image dataset from flexible endoscopy, colonoscopy and capsule endoscopy, named ERS. The collection has been labeled according to the full medical specification of 'Minimum Standard Terminology 3.0' (MST 3.0), describing all possible findings in the gastrointestinal tract (104 possible labels), extended with an additional 19 labels useful in common machine learning applications. The dataset contains around 6000 precisely and 115,000 approximately labeled frames from endoscopy videos, 3600 precise and 22,600 approximate segmentation masks, an…
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）
Physicians use Capsule Endoscopy (CE) as a non-invasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze the entire video to identify abnormalities or diseases before making diagnosis. This review task can be very tedious, time consuming and prone to error. While only as little as a single frame may capture useful content that is relevant to the physicians' final diagnosis, frames …
