Self-supervised Pretraining for Covid-19 and Other Pneumonia Detection from Chest X-ray Images

ABSTRACT

Artificial intelligence technology has made breakthroughs in computer vision and natural language processing in recent years. An important factor is that the technology analyzes tasks in a data-driven manner and automatically learns data representation from large representations of data sets for a special task. However, one of the challenges is the lacked enough labelled datasets for pneumonia detection from chest X-ray images, which usually has a small number of identically distributed labelled data for training and conflicts with data-driven deep learning. That also is the bottleneck of the development of medical imaging AI. To address this challenge, we propose a self-supervised pre-training method for Covid-19 and other pneumonia detection. The method includes pre-trained model training and transfer learning. The pre-trained model uses a self-supervised contrastive learning method to learn the general representations from source data with location-sensitive patches and multi-level features. Transfer learning includes three stages of training to specialize the representation from source data to target data. The experiments show that it has improved performance for Covid-19 detection and other pneumonia with few labelled data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.