Generative adversarial network in medical imaging: A review.
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TLDR
A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.About:
This article is published in Medical Image Analysis.The article was published on 2019-12-01 and is currently open access. It has received 1053 citations till now. The article focuses on the topics: Generative model.read more
Citations
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Journal ArticleDOI
Enhanced Pathology Image Quality with Restore-GAN.
Ruichen Rong,Shidan Wang,Xinyi Zhang,Zhuoyu Wen,Xian Cheng,Liwei Jia,Donghan M. Yang,Yang Xie,Xiaowei Zhan,Guanghua Xiao +9 more
TL;DR: Restore-Generative Adversarial Network (GAN) as discussed by the authors was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors, which can significantly improve image quality.
Patent
Method of processing medical image, and medical image processing apparatus performing the method
TL;DR: In this article, a device and a method for medical image processing are provided. But the method may include: obtaining a plurality of actual medical images corresponding to patients and including lesions; training a deep neural network (DNN) based on the plurality of real medical images; and obtaining, via the first neural network, a second medical image representing a state of the lesion at a second time point different from the first time point.
Proceedings ArticleDOI
Detection of Glaucoma using Convolutional Neural Network (CNN) with Super Resolution Generative Adversarial Network (SRGAN)
TL;DR: In this article , a CNN-SRGAN is proposed to detect Glaucoma using eye fundus images and UNet is deployed for image segmentation, the proposed system provides better accuracy in detection of Glauca.
Journal ArticleDOI
MC-GAT: multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from hyperspectral pathological images.
TL;DR: Wang et al. as mentioned in this paper proposed a multi-layer collaborative generative adversarial transformer (MC-GAT) for cholangiocarcinoma (CCA) classification from hyperspectral pathological images.
Proceedings ArticleDOI
Contrast CT image generation model using CT image of PET/CT
TL;DR: This contrast CT image generation based on deep learning is helped for a cost-effective and less-hazardous process of acquiring Contrast CT image to patients as well as more anatomical information with only CT scan.
References
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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.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.