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
Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
TL;DR: In this article , a literature review on the application of GAN in ophthalmology image domains is presented to discuss important contributions and to identify potential future research directions, and a survey on studies using GAN published before June 2021 only is presented.
Journal ArticleDOI
GAN Inversion: A Survey
TL;DR: GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model so that the image can be faithfully reconstructed from the inverted code by the generator as discussed by the authors .
Journal ArticleDOI
GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images.
Ahmed Elazab,Changmiao Wang,Changmiao Wang,Syed Jamal Safdar Gardezi,Hongmin Bai,Qingmao Hu,Tianfu Wang,Chunqi Chang,Baiying Lei +8 more
TL;DR: A novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, that outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.
Journal ArticleDOI
The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.
Tomoyuki Fujioka,Mio Mori,Kazunori Kubota,Kazunori Kubota,Jun Oyama,Emi Yamaga,Yuka Yashima,Leona Katsuta,Kyoko Nomura,Miyako Nara,Goshi Oda,Tsuyoshi Nakagawa,Yoshio Kitazume,Ukihide Tateishi +13 more
TL;DR: The basic technical knowledge and algorithms ofDeep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis are discussed.
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Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks
TL;DR: Wang et al. as discussed by the authors proposed a pathological representation network to enforce the generated and source images hold the same pathological properties in different staining domains and empirically demonstrate the effectiveness of their approach on two different unpaired histopathological datasets.
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.
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.