polyp detection
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2022 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Kristy Oden ◽  
Michelle Nelson ◽  
Laura Williams

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan Yoon ◽  
Hyoun-Joong Kong ◽  
Byeong Soo Kim ◽  
Woo Sang Cho ◽  
Jung Chan Lee ◽  
...  

AbstractComputer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.


2021 ◽  
Vol 75 (6) ◽  
pp. 540-543
Author(s):  
Daniel Kvak ◽  
Karolína Kvaková

Summary: The use of artifi cial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the effi ciency of polyp detection and even optical localization of fi ndings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided dia gnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specifi city of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artifi cial- -intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed. Key words: polyp detection – convolutional neural network – artifi cial intelligence – computer-aided dia gnosis – spatial location


2021 ◽  
Vol 2 (6) ◽  
pp. 211-219
Author(s):  
Kristen E Dougherty ◽  
Vatche J Melkonian ◽  
Grace A Montenegro

2021 ◽  
Vol 78 (6) ◽  
pp. 328-336
Author(s):  
Sang Hyun Park ◽  
Kwang Il Hong ◽  
Hyun Chul Park ◽  
Young Sun Kim ◽  
Gene Hyun Bok ◽  
...  

Author(s):  
Nguyen Chi Thanh

Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy. However, training these networks requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of endoscopy specialists. We propose a novel method for training colonoscopy image classification networks by using self-supervised visual feature learning to overcome this challenge. We adapt image denoising as a pretext task for self-supervised visual feature learning from unlabeled colonoscopy image dataset, where noise is added to the image for input, and the original image serves as the label. We use an unlabeled colonoscopy image dataset containing 8,500 images collected from the PACS system of Hospital 103 to train the pretext network. The feature exactor of the pretext network trained in a self-supervised way is used for colonoscopy image classification. A small labeled dataset from the public colonoscopy image dataset Kvasir is used to fine-tune the classifier. Our experiments demonstrate that the proposed self-supervised learning method can achieve a high colonoscopy image classification accuracy better than the classifier trained from scratch, especially at a small training dataset. When a dataset with only annotated 200 images is used for training classifiers, the proposed method improves accuracy from 72,16% to 93,15% compared to the baseline classifier.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Wang ◽  
Yihui Hu ◽  
Yanhong Luo ◽  
Xin Wang

Colorectal cancer originates from adenomatous polyps. Adenomatous polyps start out as benign, but over time they can become malignant and even lead to complications and death which will spread to adherent and surrounding organs over time, such as lymph nodes, liver, or lungs, eventually leading to complications and death. Factors such as operator’s experience shortage and visual fatigue will directly affect the diagnostic accuracy of colonoscopy. To relieve the pressure on medical imaging personnel, this paper proposed a network model for colonic polyp detection using colonoscopy images. Considering the unnoticeable surface texture of colonic polyps, this paper designed a channel information interaction perception (CIIP) module. Based on this module, an information interaction perception network (IIP-Net) is proposed. In order to improve the accuracy of classification and reduce the cost of calculation, the network used three classifiers for classification: fully connected (FC) structure, global average pooling fully connected (GAP-FC) structure, and convolution global average pooling (C-GAP) structure. We evaluated the performance of IIP-Net by randomly selecting colonoscopy images from a gastroscopy database. The experimental results showed that the overall accuracy of IIP-NET54-GAP-FC module is 99.59%, and the accuracy of colonic polyp is 99.40%. By contrast, our IIP-NET54-GAP-FC performed extremely well.


Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2264
Author(s):  
Jingjing Wan ◽  
Bolun Chen ◽  
Yongtao Yu

Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.


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