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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Proceedings ArticleDOI
Generative Modeling Helps Weak Supervision (and Vice Versa)
Benedikt Böcking,Willie Neiswanger,Nicholas Roberts,Stefano Ermon,Frederic Sala,Artur Dubrawski +5 more
TL;DR: This work proposes a model fusing programmatic weak supervision and generative adversarial networks and provides theoretical justification motivating this fusion, and is the first approach to enable data augmentation through weakly supervised synthetic images and pseudolabels.
Journal ArticleDOI
Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
Rudolf A. Werner,Takahiro Higuchi,Naoko Nose,Fujio Toriumi,Yohji Matsusaka,Ichiei Kuji,Koshino Kazuhiro +6 more
TL;DR: In this paper , a modified light-weight GAN (FastGAN) algorithm was applied to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients.
Journal ArticleDOI
Class-aware data augmentation by GAN specialisation to improve endoscopic images classification
TL;DR: In this paper , a class-aware GAN-based augmentation strategy with the help of the state-of-the-art framework StyleGAN2-ADA and fine-tuning is presented.
Journal ArticleDOI
Multimodal Deep Homography Estimation Using a Domain Adaptation Generative Adversarial Network
TL;DR: In this article , a two-step approach is proposed for multimodal image registration, where first, a generative adversarial network is trained to learn the domain transfer function between the visible and the infrared domain, thereby mitigating the impact of the visual dissimilarity between the images.
Proceedings ArticleDOI
Microscopic Fluorescence In Situ Hybridization (FISH) Image Synthesis with Generative Adversarial Networks
Gizem Dursun,Ufuk Özkaya +1 more
TL;DR: In this paper, a generative adversarial network is trained to synthesize FISH images from mask images, which can be used to provide a solution to the problem of the lack of data.
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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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.