Data Augmentation for Chest Pathologies Classification

Abstract

Diagnosis of lung pathologies from CXRs is one of the main tasks in modern image-based diagnosis. Automation of lung pathology diagnosis is greatly facilitated by recent developments in deep learning-based clinical decision making. The performance of deep learning solutions has the tendency to improve with the growing number of training X-rays, which can be artificially increased by augmentation of training X-rays. Commonly, different augmentation approaches are greedily applied to the available training data without investigating the necessity and actual contribution of individual augmentation. Our work aims to fill this gap in computerized lung pathology diagnosis and evaluate the contribution of different data augmentation approaches by leveraging the publicly available ChestX-ray14 dataset.