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

TL;DR

This paper introduces a self-supervised deep learning approach for classifying respiratory diseases from chest X-rays, achieving competitive results without extensive labeled data.

Contribution

It presents a novel self-supervised neural network pretrained on unlabeled X-ray data, improving disease classification performance with limited labeled datasets.

Findings

Achieves competitive classification accuracy on four public datasets.

Reduces dependence on large labeled datasets for training.

Demonstrates effective transfer learning from unlabeled to labeled tasks.

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.