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

Matej Gazda; Jakub Gazda; Jan Plavka; Peter Drotar

doi:10.1109/access.2021.3125324

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arXiv · 2103.03055

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

Matej Gazda·Jakub Gazda·Jan Plavka·Peter Drotar

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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.

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eess.IV cs.CV cs.NE

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Matej Gazda, Jakub Gazda, Jan Plavka, Peter Drotar. 2021-03-04. Self-supervised deep convolutional neural network for chest X-ray classification. https://doi.org/10.1109/access.2021.3125324

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