Meta Pseudo Labels for Chest X-ray Image Classification

Abstract

Deep Learning methods are getting more and more extensively applied to medical imaging tasks. Nevertheless, very frequently medical images appear unlabelled making it difficult for AI algorithms to utilize the features of the images for classification purposes. Thus, such limitations make it almost impossible to develop robust and accurate algorithm for medical image classification. In this study, we have used a semi-supervised learning method Meta Pseudo Labels which allowed us to train models with a limited amount of labelled data extracted from chest X-ray images. The approach has demonstrated promising results achieving 92.5% of accuracy on the data labelled only for 16%. Additionally, we have also implemented the Transfer Learning approach to obtain higher accuracy on data labelled for only 0.5%. The approach involved initializing the model with the weights obtained from training it on a dataset with higher portion of labelled data. The approach has been proven to be successful averagely increasing the model accuracy on 0.5% of labeled data by 26 percent.