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Chest radiography is the most common radiological examination used for the
diagnosis of thoracic diseases. Currently, automated classification of radiological images
is abundantly used in clinical diagnosis. However, each pathology has its own response
characteristic receptive field regions, which is a key problem during the classification of
chest diseases. In addition to extreme class imbalance, cases labelled as uncertain in the
dataset further complicate this task. To solve this problem, we propose a semi-supervised
learning approach known as U-SelfTrained. In this scheme, uncertain labels are left
unlabeled in the dataset; first, the model is trained on labelled instances and then on
unlabeled instances relabeling them with labels having a higher probability.
Comprehensive experimentation was carried out on the CheXpert dataset, which consists
of 223,816 frontal and lateral view CXR images of 64,740 patients with 14 diseases. The
testing accuracy is 0.877 on the CheXpert dataset, which is currently the highest score
achieved to date. This validates the effectiveness of this method for chest radiography
classification. The practical significance of this study is the implementation of AI
algorithms to assist radiologists in improving their diagnostic accuracy.