AIMC Topic: Capsule Endoscopy
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Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.
Computational and mathematical methods in medicine
Sep 11, 2021
Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical pe...
Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy.
Scientific reports
Sep 1, 2021
The manual reading of capsule endoscopy (CE) videos in small bowel disease diagnosis is time-intensive. Algorithms introduced to automate this process are premature for real clinical applications, and multi-diagnosis using these methods has not been ...
Kvasir-Capsule, a video capsule endoscopy dataset.
Scientific data
May 27, 2021
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising ben...
Deep learning for registration of region of interest in consecutive wireless capsule endoscopy frames.
Computer methods and programs in biomedicine
May 25, 2021
BACKGROUND AND OBJECTIVE: Functional gastrointestinal disorders (FGIDs) are reported as worldwide gastrointestinal (GI) diseases. GI motility assessment can assist the diagnosis of patients with intestine motility dysfunction. Wireless capsule endosc...
Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader?
Best practice & research. Clinical gastroenterology
Mar 24, 2021
Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performan...
Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.
Journal of digital imaging
Mar 16, 2021
PURPOSE: The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, more precisely, detect small intestinal abnormalities like bleeding.
Performance of a convolutional neural network for automatic detection of blood and hematic residues in small bowel lumen.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Feb 23, 2021
VR-Caps: A Virtual Environment for Capsule Endoscopy.
Medical image analysis
Feb 6, 2021
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 s...
Deep transfer learning approaches for bleeding detection in endoscopy images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 19, 2021
Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed rapidly over the last several years. One of the main limiting factors using this technology is that it produces a huge number of images, whose analysis, to be done...
Artificial intelligence and deep learning for small bowel capsule endoscopy.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Dec 27, 2020
Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algori...
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