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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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.
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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.
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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.
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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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Journal ArticleDOI
Generative Adversarial Networks: An Overview
TL;DR: Generative adversarial networks (GANs) as mentioned in this paper provide a way to learn deep representations without extensively annotated training data by deriving backpropagation signals through a competitive process involving a pair of networks.
Journal ArticleDOI
An information fidelity criterion for image quality assessment using natural scene statistics
TL;DR: This paper proposes a novel information fidelity criterion that is based on natural scene statistics and derives a novel QA algorithm that provides clear advantages over the traditional approaches and outperforms current methods in testing.
Journal ArticleDOI
Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
TL;DR: MRI scans of 92 nondemented older adults in the Baltimore Longitudinal Study of Aging provide essential information on the rate and regional pattern of age-associated changes against which pathology can be evaluated and suggest slower rates of brain atrophy in individuals who remain medically and cognitively healthy.
Posted Content
AutoAugment: Learning Augmentation Policies from Data
TL;DR: This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data).
Proceedings ArticleDOI
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
TL;DR: AttnGAN as mentioned in this paper proposes an attentional generative network to synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description.