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

• URL: http://arxiv.org/abs/2103.03055v2
• Date: Fri, 5 Mar 2021 07:34:50 GMT
• Title: Self-supervised deep convolutional neural network for chest X-ray classification
• Authors: Matej Gazda, Jakub Gazda, Jan Plavka, Peter Drotar
• Abstract summary: We propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The results obtained on four public datasets show that our approach yields competitive results without requiring large amounts of labeled training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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