Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification

doi:10.1109/ACCESS.2021.3125324

Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification

2021-01-01

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DOI：10.1109/ACCESS.2021.3125324

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Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are frequently used in the diagnosis of respiratory diseases such as pneumonia or COVID-19. In this paper, we propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. Pretraining is achieved through the contrastive learning approach by comparing representations of differently augmented input images. The learned representations are transferred to downstream tasks - the classification of respiratory diseases. We evaluate the proposed approach on two tasks for pneumonia classification, one for COVID-19 recognition and one for discrimination of different pneumonia types. The results show that our approach yields competitive results without requiring large amounts of labeled training data.

Keywords:

COVID-19

Pulmonary diseases

Convolutional neural networks

Task analysis

X-ray imaging

Lung

Feature extraction

Self-supervised learning

contrastive learning

deep learning

convolutional neural network

chest X-ray

COVID-19

Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification

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