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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Journal ArticleDOI
Gaussian Mutation-Spider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Abdul Lateef Haroon Phulara Shaik,Monica Komala Manoharan,Alok Kumar Pani,Raji Reddy Avala,Chien-Ming Chen +4 more
TL;DR: In this paper , a Gaussian mutation-Spider Monkey Optimization (GM-SMO) model was proposed for feature selection to solve overfitting and imbalanced data problems in scene classification.
Posted ContentDOI
Simulated Diagnostic Performance of Ultra-Low-Field MRI: Harnessing Open-Access Datasets to Evaluate Novel Devices
TL;DR: In this paper, the authors demonstrate a method for virtually evaluating novel imaging devices using machine learning and open-access datasets, here applied to a new, ultra-low-field strength (ULF), 64mT, portable MRI device.
Book ChapterDOI
Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs
TL;DR: In this article , a method for synthesizing cardiac MR images with plausible heart shapes and realistic appearances for the purpose of generating labeled data for deep-learning (DL) training is proposed, which breaks down the image synthesis into label deformation and label-to-image translation tasks.
Journal ArticleDOI
Prediction of OCT images of short-term response to anti-VEGF treatment for diabetic macular edema using different generative adversarial networks
TL;DR: Wang et al. as discussed by the authors evaluated the predictive performance of OCT images for the response of diabetic macular edema (DME) patients to anti-vascular endothelial growth factor (VEGF) therapy generated from baseline images using generative adversarial networks (GANs).
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.