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Unsupervised Medical Image Translation Using Cycle-MedGAN
[article]
2019
arXiv
pre-print
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However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. ...
Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. ...
In this work, we propose an adaptation of the above featurebased loss functions for the task of unsupervised image translation. ...
arXiv:1903.03374v1
fatcat:6eittos3tjhbrddjdi4optojqu
MedGAN: Medical Image Translation using GANs
[article]
2019
arXiv
pre-print
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In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. ...
MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. ...
The above-presented approaches is an overview of the utilization of GANs for medical image-to-image translation tasks. ...
arXiv:1806.06397v2
fatcat:idhwjwndrjdavckifr5hz53rcu
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
[article]
2019
arXiv
pre-print
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Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. ...
Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. ...
Fig. 1 : 1 An overview of the improved Cycle-MedGAN framework with randomly sampled input images x and y.
Fig. 2 : 2 The proposed generator architecture with self-attention blocks. ...
arXiv:1910.05597v1
fatcat:guozjy4ptzguplmafau4ck6fry
Uncertainty-Guided Progressive GANs for Medical Image Translation
[article]
2021
arXiv
pre-print
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In this work, we propose an uncertainty-guided progressive learning scheme for image-to-image translation. ...
By incorporating aleatoric uncertainty as attention maps for GANs trained in a progressive manner, we generate images of increasing fidelity progressively. ...
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for support. ...
arXiv:2106.15542v2
fatcat:tm2xihczzrbs7cbhe7bq5k6nbe
Protect and Extend – Using GANs for Synthetic Data Generation of Time-Series Medical Records
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2402.14042v1 fatcat:fisgf3msdfgfvhdhbirnufri5u
This research compares state-of-the-art GAN-based models for synthetic data generation to generate time-series synthetic medical records of dementia patients which can be distributed without privacy concerns ...
Generative Adversarial Networks (GANs) have been used for generating synthetic datasets, especially GAN frameworks adhering to the differential privacy phenomena. ...
GAN-based Approaches One of the earlier contributions applying GANs for synthetic medical records generation is medGAN [8] . ...
arXiv:2402.14042v2
fatcat:4hiqnx5kgnaylnfcafsxbwdevq
Hybrid Segmentation Algorithm for Medical Image Segmentation Based On Generating Adversarial Networks, Mutual Information And Multi-scale Information
2020
IEEE Access
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This paper proposes 3D-MedGAN, MLU-Net and Info-Max-Net models for overcoming the lack of labeled data and extracting the multi-level feature of images in medical image segmentation. 3D-MedGAN is aimed ...
Considering that the quality of images generated by 3D-MedGAN are not as good as the original images, we combine the 3D-MedGAN, MLU-Net, and Info-Max-Net to improve the sensitivity and the power of feature ...
We referred to this model as 3D-Medical GAN. We used the 3D Unet [12] network as our generator G. ...
doi:10.1109/access.2020.3005384
fatcat:nqcfigdr5zho7naagd5uioso74
A Review of Generative Adversarial Networks for Computer Vision Tasks
2024
Electronics
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Generative adversarial networks (GANs) offer a possible solution to artificially expand datasets, providing a basic resource for applications requiring large and diverse data. ...
Through this research, we aim to contribute to the broader understanding and application of GANs in scenarios where dataset scarcity poses a significant obstacle, particularly in medical imaging applications ...
The ESRGAN produces less blurry and better quality images than SRGAN [21] .
MedGAN MedGAN appeared as a solution to the problem that not enough medical data is available for experiments. ...
doi:10.3390/electronics13040713
fatcat:f3tm6vzlbvf7rozpl3djhebkpi
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.14679v1 fatcat:fzh5o42thvan5mtruj2c3jqja4
image, text, and sound. ...
Synthetic data generation is a promising solution to mitigate these risks, often relying on deep generative models such as Generative Adversarial Networks (GANs). ...
Each component has two hidden layers with the size of 128. • medGAN: A GAN-based model adapted to generate discrete tabular EHRs, by incorporating an additional autoencoder and minibatch-averaging to cope ...
arXiv:2302.14679v2
fatcat:2l2ihqwcbvakziow3jfx3fjrsa
Beyond Differential Privacy: Synthetic Micro-Data Generation with Deep Generative Neural Networks
[chapter]
2020
Security and Privacy From a Legal, Ethical, and Technical Perspective
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Recent advances in generative modeling, based on large scale deep neural networks, provide a novel approach for sharing individual-level datasets (micro-data) without privacy concerns. ...
generate "synthetic data" that accurately reflects these statistical patterns, yet contain none of the original data itself, and thus can be safely shared for analysis and modeling without compromising ...
Marlene Grenon for many hours of discussions on the topic of synthetic data generation. ...
doi:10.5772/intechopen.92255
fatcat:l7zf3updcnbvzb5b6rk4eoyjjy
Minority Class Oversampling for Tabular Data with Deep Generative Models
[article]
2020
arXiv
pre-print
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We take proposals of deep generative models, including our own, and study the ability of these approaches to provide realistic samples that improve performance on imbalanced classification tasks via oversampling ...
Without accounting for the imbalance, common classifiers perform poorly and standard evaluation metrics mislead the practitioners on the model's performance. ...
Later, [7] adapted the technique to GANs for sequences of discrete elements. ...
arXiv:2005.03773v2
fatcat:ckdi3mgzpfbmfalyuicnz3rttq
Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
[article]
2020
arXiv
pre-print
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Recently, deep learning approaches for MR motion artifact correction have been extensively studied. ...
For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. ...
Armanious et al [30] suggested MedGAN for medical image translation, and applied it to MRI motion artifact correction [28] , [30] . ...
arXiv:2011.06337v1
fatcat:ml7jvwxxprgbzeth2aijcevlcu
Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics
2020
IMIA Yearbook of Medical Informatics
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Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. ...
Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. ...
Acknowledgments The section editors would like to thank Adrien Ugon for supporting the external review process, and the external reviewers for their input on the candidate best papers. ...
doi:10.1055/s-0040-1702004
pmid:32823307
fatcat:azf464zzrzff3d6okstspbhi7a
Narrative review of generative adversarial networks in medical and molecular imaging
2021
Annals of Translational Medicine
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GANs are promising tools for medical and molecular imaging. The progress of model architectures and their applications should continue to be noteworthy. ...
Generative adversarial networks (GANs) are techniques to synthesize images based on artificial neural networks and deep learning. ...
Domain adaptation Supervised learning for medical image analysis requires appropriate labels and annotation by medical experts, which takes an enormous amount of time and effort. ...
doi:10.21037/atm-20-6325
pmid:34268434
pmcid:PMC8246192
fatcat:6dfpalmijjcrnmlb7e6ppycloq
Generative Adversarial Networks Applied to Observational Health Data
[article]
2020
arXiv
pre-print
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Generative Adversarial Networks (GAN) have Generative Adversarial Networks (GAN) have recently emerged as a groundbreaking approach to efficiently learn generative models that produce realistic Synthetic ...
We conducted a review of GAN algorithms for OHD in the published literature, and report our findings here. ...
Primarily, algorithms developed for images and text in other fields were easily repurposed for medical equivalents. ...
arXiv:2005.13510v1
fatcat:v6ukcv7td5gddalp4svwa2z2di
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods
2021
Journal of Personalized Medicine
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Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. ...
We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer ...
divergence-based approaches for domain adaptation by using a two-stream Rozantsev et al. [181] Non-medical images Synthetic, real image domains CNN architecture (one in the source domain with synthetic ...
doi:10.3390/jpm11090842
pmid:34575619
pmcid:PMC8472571
fatcat:2ngorzmaw5alrpj7deecvvf4au
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