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

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.

arXiv commentThe work was published by IEEE Access. DOI: 10.1109/ACCESS.2021.3125324

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• Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
• Auto-outlier Fusion Technique for Chest X-ray classification with Multi-head Attention Mechanism