Journal ArticleDOI
Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.
TLDR
Experimental results show that the proposed deep convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.About:
This article is published in Computers in Biology and Medicine.The article was published on 2018-08-01. It has received 96 citations till now. The article focuses on the topics: Convolutional neural network.read more
Citations
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Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
Journal ArticleDOI
An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.
TL;DR: It has been determined that brain tumors have been better segmented and removed using SR-FCM method and the accuracy rate is greater 10% than the rate of recognition of brain tumors segmented with fuzzy C-means (FCM) without SR.
Journal ArticleDOI
A Review of the Deep Learning Methods for Medical Images Super Resolution Problems
TL;DR: Deep learning methods are briefly introduced, a number of important deep learning approaches to solve super resolution problems are presented, different architectures as well as up-sampling operations will be introduced and the challenges to overcome are presented.
Journal ArticleDOI
Multi-Contrast Super-Resolution MRI Through a Progressive Network
TL;DR: The proposed networks integrate multi-contrast information in a high-level feature space and optimize the imaging performance by minimizing a composite loss function, which includes mean-squared-error, adversarial loss, perceptual loss, and textural loss.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
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.
Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Journal ArticleDOI
Image Super-Resolution Using Deep Convolutional Networks
TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
Proceedings ArticleDOI
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi,Jose Caballero,Ferenc Huszar,Johannes Totz,Andrew Peter Aitken,Rob Bishop,Daniel Rueckert,Zehan Wang +7 more
TL;DR: This paper presents the first convolutional neural network capable of real-time SR of 1080p videos on a single K2 GPU and introduces an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output.