Bibliographic Details
| Title: |
Self-supervised deep convolutional neural network for chest X-ray classification |
| Authors: |
Gazda, Matej, Gazda, Jakub, Plavka, Jan, Drotar, Peter |
| Publication Year: |
2021 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing |
| More Details: |
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.
Comment: The work was published by IEEE Access. DOI: 10.1109/ACCESS.2021.3125324 |
| Document Type: |
Working Paper |
| DOI: |
10.1109/ACCESS.2021.3125324 |
| Access URL: |
http://arxiv.org/abs/2103.03055 |
| Accession Number: |
edsarx.2103.03055 |
| Database: |
arXiv |
