Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images
TLDR
This review summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks.Abstract:
Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.read more
Citations
More filters
Journal ArticleDOI
Deep Learning for Medical Anomaly Detection – A Survey
TL;DR: A coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms and a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions are provided.
Journal ArticleDOI
Medical image analysis based on deep learning approach.
Muralikrishna Puttagunta,S. Ravi +1 more
TL;DR: Deep Learning Approach (DLA) has been widely used in medical imaging to detect the presence or absence of the disease as discussed by the authors, and most of the implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images.
Journal ArticleDOI
Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial.
Lianlian Wu,Xinqi He,Mei Liu,Huaping Xie,Ping An,Jun Zhang,Heng Zhang,Yaowei Ai,Qiaoyun Tong,Mingwen Guo,Manling Huang,Cunjin Ge,Zhi Yang,Jingping Yuan,Jun Liu,Wei Zhou,Xiaoda Jiang,Xu Huang,Ganggang Mu,Xinyue Wan,Yanxia Li,Hongguang Wang,Yonggui Wang,Hongfeng Zhang,Di Chen,Dexin Gong,Jing Wang,Li Huang,Jia Li,Liwen Yao,Yijie Zhu,Honggang Yu +31 more
TL;DR: In this paper, an endoscopy system using deep convolutional neural networks and deep reinforcement learning was used to detect early gastric cancer (EGC) in a multicenter randomized controlled trial.
Journal ArticleDOI
Deep learning based image classification for intestinal hemorrhage
TL;DR: A supervised learning ensemble to detect the bleeding in the images of Wireless Capsule Endoscopy accurately finds out the best possible combination of attributes required to classify bleeding symptoms in endoscopy images.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.