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Self-supervised deep convolutional neural network for chest X-ray classification

2021-03-04 14:28:37
Matej Gazda, Jakub Gazda, Jan Plavka, Peter Drotar
arXiv_CV
           
Abstract

Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are almost always used in the diagnosis of respiratory diseases such as pneumonia or the recent COVID-19. In this paper, we propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The learned representations are transferred to downstream task - the classification of respiratory diseases. The results obtained on four public datasets show that our approach yields competitive results without requiring large amounts of labeled training data.

Abstract (translated)

URL

https://arxiv.org/abs/2103.03055

PDF

https://arxiv.org/pdf/2103.03055.pdf

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