Generative adversarial network in medical imaging: A review.
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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
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Book ChapterDOI
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
TL;DR: In this paper , a sequence-aware diffusion model (SADM) is proposed for the generation of longitudinal medical images, which enables learning longitudinal dependency even with missing data during training and allows autoregressive generation of a sequence of images during inference.
Posted Content
Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
TL;DR: This article proposes a new framework that normalizes the stain style for cytopathological images through a stain removal module and a multi-stage domain adversarial style reconstruction module, and converts colorful images into grayscale images with a color-encoding mask.
Journal ArticleDOI
Games of GANs: game-theoretical models for generative adversarial networks
Monireh Mohebbi Moghaddam,Bahar Boroomand,Mohammad Jafar Pour Jalali,Arman Zareian,Alireza Daeijavad,Mohammad Hossein Manshaei,Marwan Krunz +6 more
TL;DR: This article reviewed the literature on the game-theoretic aspects of GANs and addressed how game theory models can address specific challenges of generative models and improve the GAN's performance.
Journal ArticleDOI
Enhancement of frequency scanning interferometry signal for non-cooperative target based on generative adversarial network
TL;DR: This paper proposes a non-cooperative target FSI signal enhancement method based on a generative adversarial network and results reveal that the SNR of the non- cooperative target signal is improved and the signal waveform is satisfactorily corrected.
Journal ArticleDOI
Towards precision sleep medicine: Self-attention GAN as an innovative data augmentation technique for developing personalized automatic sleep scoring classification
TL;DR: In this paper , a self-attention generative adversarial network (SAGAN) was applied as an advanced data augmentation technique to propose an improved personalized automatic sleep scoring classification.
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.