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
A survey on Image Data Augmentation for Deep Learning
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
Journal ArticleDOI
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
TL;DR: This article provides a detailed review of the solutions above, summarizing both the technical novelties and empirical results, and compares the benefits and requirements of the surveyed methodologies and provides recommended solutions.
Journal ArticleDOI
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Journal ArticleDOI
MedGAN: Medical image translation using GANs
Karim Armanious,Karim Armanious,Chenming Jiang,Marc Fischer,Thomas Küstner,Tobias Hepp,Konstantin Nikolaou,Sergios Gatidis,Bin Yang +8 more
TL;DR: A new framework, named MedGAN, is proposed for medical image-to-image translation which operates on the image level in an end- to-end manner and outperforms other existing translation approaches.
Journal ArticleDOI
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Mohammad Jamshidi,Ali Lalbakhsh,Jakub Talla,Zdenek Peroutka,Farimah Hadjilooei,Pedram Lalbakhsh,Morteza Jamshidi,Luigi La Spada,Mirhamed Mirmozafari,Mojgan Dehghani,Asal Sabet,Saeed Roshani,Sobhan Roshani,Nima Bayat-Makou,Bahare Mohamadzade,Zahra Malek,Alireza Jamshidi,Sarah Kiani,Hamed Hashemi-Dezaki,Wahab Mohyuddin +19 more
TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers.
References
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Proceedings Article
Wasserstein Generative Adversarial Networks
TL;DR: This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.
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GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium
Martin Heusel,Hubert Ramsauer,Thomas Unterthiner,Bernhard Nessler,Günter Klambauer,Sepp Hochreiter +5 more
TL;DR: In this article, a two time-scale update rule (TTUR) was proposed for training GANs with stochastic gradient descent on arbitrary GAN loss functions, which has an individual learning rate for both the discriminator and the generator.
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A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
Proceedings ArticleDOI
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
TL;DR: In this article, a volumetric, fully convolutional neural network (FCN) was proposed to predict segmentation for the whole volume at one time, which can deal with situations where there is a strong imbalance between the number of foreground and background voxels.