GANs 'N Lungs: improving pneumonia prediction

We propose a novel method to improve deep learning model performance on highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve balanced dataset. We show that data augmentation with GAN helps to improve accuracy of pneumonia binary classification task even if the generative netw...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Malygina, Tatiana, Ericheva, Elena, Drokin, Ivan
Format: Article
Language:English
Subjects:
Data augmentation
Datasets
Generative adversarial networks
Machine learning
Pneumonia
Online Access:Get full text
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Summary:We propose a novel method to improve deep learning model performance on highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve balanced dataset. We show that data augmentation with GAN helps to improve accuracy of pneumonia binary classification task even if the generative network was trained on the same training dataset.
ISSN:2331-8422