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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Generative Portfolio Optimization with Attention-Powered Sequential Learning
Chuting Sun,Qi Wu,Xing Yan +2 more
TL;DR: In this paper , the authors proposed a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the Attention-GRU network for the dynamic modeling.
Proceedings ArticleDOI
Evaluating generative stochastic image models using task-based image quality measures
Varun A. Kelkar,Dimitrios Gotsis,Rucha Deshpande,Frank J. Brooks,Prabhat Kc,K Myers,Rongping Zeng,Mark A. Anastasio +7 more
TL;DR: In this article , canonical stochastic image models (SIMs) were employed to evaluate GAN-based SIMs with respect to detection, detection-localization, and detection-estimation tasks.
Journal ArticleDOI
Dual-Enhanced Registration for Field of View Ultrasound Sonography
TL;DR: Dual-enhanced EFOV-US method is proposed that overcomes the limitation and produces higher quality results in Extended Field of View Ultrasound Sonography and the experimental results show that the proposed method is effective and practical.
Journal ArticleDOI
End-to-End 3D Liver CT Image Synthesis from Vasculature Using a Multi-Task Conditional Generative Adversarial Network
TL;DR: Wang et al. as mentioned in this paper used a mask of vascular segmentation as the input to guide the 3D CT image generation, reducing the calculation of a large number of backgrounds, thus making the model more focused on the region of the liver.
References
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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.
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.
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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.