Self-supervised deep convolutional neural network for chest X-ray classification
This work addresses the challenge of limited labeled medical data for diagnosing respiratory diseases like pneumonia and COVID-19, though it appears incremental as it builds on existing self-supervised methods.
The authors tackled the problem of classifying respiratory diseases from chest X-rays by proposing a self-supervised deep neural network pretrained on unlabeled data, achieving competitive results on four public datasets without needing large labeled datasets.
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.