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
A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation
Nikoletta-Maria Koutroumpa,Konstantinos D. Papavasileiou,Anastasios G. Papadiamantis,Georgia Melagraki,Antreas Afantitis +4 more
TL;DR: In this article , a systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments and highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug development cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles.
Journal ArticleDOI
Estimation of Tissue Oxygen Saturation from RGB images and Sparse Hyperspectral Signals based on Conditional Generative Adversarial Network
TL;DR: In this paper, a dual-input conditional generative adversarial network (cGAN) was proposed to directly estimate StO2 by fusing features from both RGB and sHSI.
Journal ArticleDOI
A surface roughness grade recognition model for milled workpieces based on deep transfer learning
TL;DR: Deep AlexCORAL as discussed by the authors is a surface roughness grade recognition model for milled workpieces based on deep transfer learning, which automatically extracts more general roughness-related features to reduce the amount of data required by the model and also the difference in data distribution between the source domain (training set) and the target domain (testing set).
Proceedings ArticleDOI
Glioma Segmentation Strategies in 5G Teleradiology
TL;DR: An edge computing (EC) driven 5G teleradiology framework is proposed to segment glioma accurately, and experiments show that the proposed data augmentation strategy improves the average Dice score of Dense U-net compared to the conventionalData augmentation method.
Journal ArticleDOI
Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery
TL;DR: ProvoGAN as mentioned in this paper proposes a progressive volumetrization strategy for generative models that serially decomposes complex volumetric image recovery tasks into successive cross-sectional mappings task-optimally ordered across individual rectilinear dimensions.
References
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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.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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.
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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.