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One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Data augmentation for face recognition
Jiang-Jing Lv, Xiao-Hu Shao, Jia-Shui Huang, Xiang-Dong Zhou, and Xi Zhou · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Gan augmentation: Augmenting training data using generative adversarial networks
Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, and Daniel Rueckert · 2018
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Data augmentation via variational auto-encoders
Unai Garay-Maestre, Antonio-Javier Gallego, and Jorge Calvo-Zaragoza · 2019
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Combining noise-to-image and image-to-image gans: Brain mr image augmentation for tumor detection
Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, and Hideaki Hayashi · 2019
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Brain tumor detection using convolutional neural network
Hossain Tonmoy, Shishir Fairuz Shadmani, Ashraf Mohsena, MD Al Nasim Abdullah, and Muhammad Shah Faisal · 2019
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A review of the application of deep learning in medical image classification and segmentation
Lei Cai, Jingyang Gao, and Di Zhao · 2020
Cited alongside, same era.
Variational autoencoder as a method of data augmentation
T. Fuertes · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Mm-gan: 3d mri data augmentation for medical image segmentation via generative adversarial networks
Yi Sun, Peisen Yuan, and Yuming Sun · 2020
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Insufficient data generative model for pipeline network leak detection using generative adversarial networks
The role of generative adversarial networks in brain mri: a scoping review
Hazrat Ali, Md Rafiul Biswas, Farida Mohsen, Uzair Shah, Asma Alamgir, Osama Mousa, and Zubair Shah · 2022
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Mri brain tumor classification technique using fuzzy c-means clustering and artificial neural network
Angona Biswas and Md Saiful Islam · 2022
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Generative adversarial networks in medical image augmentation: a review
Yizhou Chen, Xu-Hua Yang, Zihan Wei, Ali Asghar Heidari, Nenggan Zheng, Zhicheng Li, Huiling Chen, Haigen Hu, Qianwei Zhou, and Qiu Guan · 2022
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Towards federated learning based contraband detection within airport baggage x-rays
Sai Puppala, Ismail Hossain, and Sajedul Talukder · 2022
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Data augmentation using variational autoencoders for improvement of respiratory disease classification
Jane Saldanha, Shaunak Chakraborty, Shruti Patil, Ketan Kotecha, Satish Kumar, and Anand Nayyar · 2022
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Huaguang Zhang, Xuguang Hu, Dazhong Ma, Rui Wang, and Xiangpeng Xie · 2020
Cited alongside, same era.
Data augmentation for skin lesion using self-attention based progressive generative adversarial network
Ibrahim Saad Aly Abdelhalim, Mamdouh Farouk Mohamed, and Yousef Bassyouni Mahdy · 2021
Cited alongside, same era.
The prominence of artificial intelligence in covid-19
MD Nasim, Aditi Dhali, Faria Afrin, Noshin Tasnim Zaman, and Nazmul Karim · 2021
Cited alongside, same era.
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Vignesh Sampath, Iñaki Maurtua, Juan Jose Aguilar Martin, and Aitor Gutierrez · 2021
Cited alongside, same era.
Brain tumor segmentation using enhanced u-net model with empirical analysis
Md Abdullah Al Nasim, Abdullah Al Munem, Maksuda Islam, Md Aminul Haque Palash, Md Mahim Anjum Haque, and Faisal Muhammad Shah · 2022
Cited alongside, same era.
Brain tumor detection using convolutional neural network
Tonmoy Hossain, Fairuz Shadmani Shishir, Mohsena Ashraf, MD Abdullah Al Nasim4&, and Faisal Muhammad Shah
Cited in the paper.
Brain tumor segmentation techniques on medical images-a review
Faisal Muhammad Shah, Tonmoy Hossain, Mohsena Ashraf, Fairuz Shadmani Shishir, MD Abdullah Al Nasim, and Md Hasanul Kabir
Cited in the paper.
The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey
Rick Sauber-Cole and Taghi M Khoshgoftaar · 2022
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Federated learning-based contraband detection within airport baggage x-rays
Sajedul Talukder, Sai Puppala, and Ismail Hossain · 2022
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A novel hierarchical federated learning with self-regulated decentralized clustering
Sajedul Talukder, Sai Puppala, and Ismail Hossain · 2022
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Generation of synthetic ground glass nodules using generative adversarial networks (gans)
Zhixiang Wang, Zhen Zhang, Ying Feng, Lizza EL Hendriks, Razvan L Miclea, Hester Gietema, Janna Schoenmaekers, Andre Dekker, Leonard Wee, and Alberto Traverso · 2022
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Collaborative differentially private federated learning framework for the prediction of diabetic retinopathy
Ismail Hossain, Sai Puppala, and Sajedul Talukder · 2023
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