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Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs [article]

Bradley Segal, David M. Rubin, Grace Rubin, Adam Pantanowitz
2021 arXiv   pre-print
Chest x-rays are a vital tool in the workup of many patients.  ...  We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples.  ...  We evaluate the applicability of the Fréchet Inception Distance to the evaluation of synthetic chest x-rays and find that the underlying network is capable of providing a meaningful metric for generate  ... 
arXiv:2010.03975v2 fatcat:7incgpatqzfjri2jvyljadv5bm

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

Bradley Segal, David M. Rubin, Grace Rubin, Adam Pantanowitz
2021 SN Computer Science  
Chest X-rays are a vital diagnostic tool in the workup of many patients.  ...  Previous work has sought to address these concerns by creating class-specific generative adversarial networks (GANs) that synthesise images to augment training data.  ...  We evaluate the applicability of the Fréchet Inception Distance to the evaluation of synthetic chest X-rays and find that the underlying network is capable of providing a meaningful metric for generate  ... 
doi:10.1007/s42979-021-00720-7 pmid:34104898 pmcid:PMC8176276 fatcat:p2tgy6s3y5dsfoyebe5saoo3my

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community.  ...  Salehinejad et al (2018) [405] DCGAN Private Chest X-Rays Data augmentation Accuracy(%): 70.87 92.10 Chest pathology CLF using synthetic data.  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Deep learning in medical image registration

Xiang Chen, Andres Diaz-Pinto, Nishant Ravikumar, Alejandro Frangi
2020 Progress in Biomedical Engineering  
to open challenges and as yet unmet clinical needs that could shape future research directions.  ...  This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights  ...  Acknowledgments The Royal Academy of Engineering supports the work of A F F through a Chair in Emerging Technologies (CiET1819\19) and the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical  ... 
doi:10.1088/2516-1091/abd37c fatcat:74w7ra4f7nfrrpfk2ifvmijntq

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation [article]

August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, Patrick Schwab
2021 arXiv   pre-print
Here, we utilize Generative Adversarial Networks (GANs) to create derived medical imaging datasets consisting entirely of synthetic patient data.  ...  We assess the quality of synthetic data generated by two GAN models for chest radiographs with 14 different radiology findings and brain computed tomography (CT) scans with six types of intracranial hemorrhages  ...  Recently, new generative machine-learning approaches, such as Generative Adversarial Networks (GANs), have demonstrated the capability to generate realistic, high-resolution image datasets [23] .  ... 
arXiv:2012.03769v3 fatcat:rvljbwpnbnbujawlf2s2vefbdq

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Mahdi Rezaei, Mahsa Shahidi
2020 Social Science Research Network  
This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis  ...  We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn.  ...  [203] proposes a method for X-ray medical image segmentation using task driven generative adversarial networks.  ... 
doi:10.2139/ssrn.3624379 fatcat:yifnxv46rjf6pgndowkxzmo5o4

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Thanh Thi Nguyen
2020 Figshare  
We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems.  ...  This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle.  ...  Other techniques such as autoencoders, recurrent neural network and generative adversarial network are crucial components of many prominent natural language processing tools.  ... 
doi:10.6084/m9.figshare.12127020.v4 fatcat:x6vgvncjubgq3hesforqxcozui

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Mahdi Rezaei, Mahsa Shahidi
2020 Intelligence-Based Medicine  
This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis  ...  We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn.  ...  [203] proposes a method for X-ray medical image segmentation using task driven generative adversarial networks.  ... 
doi:10.1016/j.ibmed.2020.100005 pmid:33043311 pmcid:PMC7531283 fatcat:qzyaf7gpufhermyg5gvank5cja

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Thanh Thi Nguyen
2020 figshare.com  
We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems.  ...  This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle.  ...  Cohen's GitHub [80] Chest X-ray and CT images https://github.com/ieee8023/covid-chestxray-dataset European Society of Radiology Chest X-ray and CT images https://www.eurorad.org/advanced-search?  ... 
doi:10.6084/m9.figshare.12127020.v6 fatcat:uhlfvss2cvcnphivhgqns7nl7q

Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis [article]

Christoph Berger, Magdalini Paschali, Ben Glocker, Konstantinos Kamnitsas
2021 arXiv   pre-print
We then evaluate their capabilities on the challenging task of disease classification using chest X-rays.  ...  Our results provide useful insights for developing the next generation of OOD detection methods.  ...  Results In 4 Benchmarking on the X-ray Lung Pathology Dataset Experimental Setup Dataset: To simulate a realistic OOD detection task in a clinical setting, we use subsets of the CheXpert X-ray lung  ... 
arXiv:2107.02568v1 fatcat:26z53wikwjezvfkznxezke2g2i

Quality-aware semi-supervised learning for CMR segmentation [article]

Bram Ruijsink, Esther Puyol-Anton, Ye Li, Wenja Bai, Eric Kerfoot, Reza Razavi, Andrew P. King
2020 arXiv   pre-print
We have used these clinical assessments in previous works to create robust quality-control (QC) classifiers for automated cardiac magnetic resonance (CMR) analysis.  ...  We evaluate our approach in two CMR segmentation tasks (aortic and short axis cardiac volume segmentation) using UK Biobank data and two commonly used network architectures (U-net and a Fully Convolutional  ...  This research has been conducted using the UK Biobank Resource under Application Number 17806.  ... 
arXiv:2009.00584v1 fatcat:ougvwbtg7rfmhj4sprychu5lmy

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
Conclusion The chest X-ray is one of the most common imaging examinations in clinical diagnosis, and chest X-ray images are useful to diagnose different thorax diseases.  ...  Results In this study, the open dataset (NIH Chest X-ray dataset) was employed to diagnose thorax diseases in X-ray images, which contained 112,120 frontal-view chest X-ray images, labels with 14 common  ...  Artificial intelligence coronary calcium scoring in low dose chest CT-Ready to go?  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation [article]

Christopher Bowles, Roger Gunn, Alexander Hammers, Daniel Rueckert
2018 arXiv   pre-print
In this paper we propose combining both labelled and unlabelled data within a GAN framework, before using the resulting network to produce images for use when training a segmentation network.  ...  Whilst of less value than labelled images, these unlabelled images can contain potentially useful information.  ...  A similar approach is taken in [3] where synthetic normal and abnormal chest radiographs are generated using two DCGAN-like architectures, and in [4] , synthetic chest X-rays are generated in order  ... 
arXiv:1811.10669v1 fatcat:xt3sdm3ui5gnlorghn7f6q7c3u

Learning Disentangled Representations in the Imaging Domain [article]

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
2022 arXiv   pre-print
A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task.  ...  Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.  ...  Generative Adversarial Networks An alternative to disentangling through reconstructing the input is image synthesis using Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) .  ... 
arXiv:2108.12043v5 fatcat:cbpmp6pbajhjvjzovulswuj2wy

Theory and practical guidance for effective de-implementation of practices across health and care services: a realist synthesis

Christopher R Burton, Lynne Williams, Tracey Bucknall, Denise Fisher, Beth Hall, Gill Harris, Peter Jones, Matthew Makin, Anne Mcbride, Rachel Meacock, John Parkinson, Jo Rycroft-Malone (+1 others)
2021 Health Services and Delivery Research  
Any realist inquiry generates findings that are essentially cumulative and should be developed through further investigation that extends the range of sources into, for example, clinical research and further  ...  Design A realist synthesis was conducted using an iterative stakeholder-driven four-stage approach.  ...  rate of 1.07 (interquartile range 0.94-1.21; p < 0.001) chest X-rays per patient per day.  ... 
doi:10.3310/hsdr09020 fatcat:vdjwikwp6jau7msbaablpwpkea
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