AIMC Topic: Intestinal Polyps
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Artificial intelligence in intestinal polyp and colorectal cancer prediction.
Cancer letters
May 19, 2023
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic a...
PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation.
Sensors (Basel, Switzerland)
Jun 21, 2022
In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural net...
Single Shot Multibox Detector Automatic Polyp Detection Network Based on Gastrointestinal Endoscopic Images.
Computational and mathematical methods in medicine
Nov 5, 2021
PURPOSE: In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single S...
Gastric polyp detection in gastroscopic images using deep neural network.
PloS one
Apr 28, 2021
This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect ...
Unravelling the effect of data augmentation transformations in polyp segmentation.
International journal of computer assisted radiology and surgery
Sep 28, 2020
PURPOSE: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a...
Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.
Computational and mathematical methods in medicine
Aug 18, 2020
METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model is used to detect whether the image contains lesions: CRC, colorectal adenoma (CRA), and colorectal...
How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.
Gastrointestinal endoscopy clinics of North America
Apr 11, 2020
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improv...
Quality assurance of computer-aided detection and diagnosis in colonoscopy.
Gastrointestinal endoscopy
Mar 26, 2019
Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 m...
Reliable Label-Efficient Learning for Biomedical Image Recognition.
IEEE transactions on bio-medical engineering
Dec 27, 2018
The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. To acquire accurate labels as the gold standard, multiple observers with specific expertise are required for both annotation and proofread...
Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.
The lancet. Gastroenterology & hepatology
Dec 6, 2018
Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby...
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