GANs 'N Lungs: improving pneumonia prediction

Autor:	Malygina, Tatiana, Ericheva, Elena, Drokin, Ivan
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as an extended abstract for MIDL 2019 [arXiv:1907.08612]
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.00433 Zobrazit plný text záznamu View this record from Arxiv