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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Proceedings ArticleDOI
Multi-contrast High Quality MR Image Super-Resolution with Dual Domain Knowledge Fusion
TL;DR: Zhang et al. as discussed by the authors proposed Edge Mask Transformer UNet (EMFU) to generate global details and texture representation of target domain by re-distributing the embedding tensors, so that the network allocates more attention to image edge area.
Journal ArticleDOI
Virtual contrast enhancement for CT scans of abdomen and pelvis
Jingya Liu,Yingli Tian,Cihan Duzgol,Oguz Akin,Ahmet Muhtesem Agildere,Kemal Murat Haberal,Mehmet Coşkun +6 more
TL;DR: In this paper , a generative adversarial network (GAN) based framework was proposed to automatically synthesize contrast-enhanced CTs directly from the non-contrast CTs in the abdomen and pelvis region.
Proceedings ArticleDOI
Investigating the Potential of Auxiliary-Classifier Gans for Image Classification in Low Data Regimes
TL;DR: It is demonstrated that AC-GANs show promise in image classification, achieving competitive performance with standard CNNs, and can be employed as an ’all-in-one’ framework with particular utility in the absence of large amounts of training data.
Posted Content
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.
Richard Osuala,Kaisar Kushibar,Lidia Garrucho,Akis Linardos,Zuzanna Szafranowska,Stefan Klein,Ben Glocker,Oliver Diaz,Karim Lekadir +8 more
TL;DR: In this article, the authors assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning.
Posted ContentDOI
Generative Adversarial Networks Applied to Observational Health Data
TL;DR: A review of GAN algorithms for OHD in the published literature is conducted, and the findings are reported here.
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.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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.