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
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Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models
TL;DR: In this article , the authors comprehensively evaluated four GANs (progressive GAN, StyleGAN 1-3, and a diffusion model) for the task of brain tumor segmentation and showed that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images.
Journal ArticleDOI
Bright-field to fluorescence microscopy image translation for cell nuclei health quantification
TL;DR: In this article , the cross-attention conditional generative adversarial network (XAcGAN) was proposed to solve the image translation task using supervised learning and combines attention-based networks to explore spatial information during translation.
Journal ArticleDOI
Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling
TL;DR: In this paper , the authors proposed a conditional generative adversarial network (CGAN) to generate new images to increase the number of images available for training a CNN model to perform age estimation.
Journal ArticleDOI
Corneal endothelial image segmentation training data generation using GANs. Do experts need to annotate?
Adrian Kucharski,Anna Fabijańska +1 more
TL;DR: In this article , a two-step pipeline for synthesizing training patches is proposed, where a custom mosaic generator creates a grid that mimics a binary map of endothelial cell edges, and the synthetic cell edges are then input to the GANs, which is trained to generate corneal endothelial image patches from the corresponding edge labels.
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.