Improving Low Resource Chest X-ray Classification Accuracy by Combining Data Augmentation and Weakly Supervised Learning

Minkyu Park, Juntae Kim

http://doi.org/10.5626/JOK.2021.48.9.1027

Deep learning-based medical image analysis technology has been developed to the extent that it shows an accuracy surpassing the ability of a human radiologist. However, labeling sample data for use in learning medical images requires human experts and a great deal of time and expense. In addition, the training data for medical images has an unbalanced data distribution in many cases. For example, in the case of the ChestX-ray14 dataset, the difference between the number of data for infiltration and hernia is about 87 times. In this study, we proposed a method that combines the data augmentation algorithm Mixup and weakly supervised learning to improve the performance of data-imbalanced chest X-ray classification. The proposed method is to apply Mixup algorithm to a small number of labeled data and a large number of unlabeled data to alleviate data imbalance and performs curriculum learning that effectively utilizes the unlabeled data while cycling through the teacher model and the student model. Experimental results in an environment with a small number of labeled data and a large number of unlabeled data that can be considered in the medical field showed that the classification performance was improved by combining data augmentation and weakly supervised learning and that the cyclic curriculum learning was effective.