Performance improvement in multi-label thoracic abnormality classification of chest X-rays with noisy labels.

Abstract

This study aimed at developing a deep learning-based method for multi-label thoracic abnormality classification on frontal view chest X-ray (CXR). To improve the performance of classification, issues of class imbalance, noisy labels and ensemble of networks are addressed in the paper. The experiments were performed on a public dataset called Chest X-ray 14 (CXR14), which includes 112,120 frontal view CXRs from 30,805 patients. We came up with an ensemble learning framework to improve the classification and a noisy label detection method to detect the CXRs with noisy labels. The detected CXRs were reviewed by two board-certificated radiologists in a consensus fashion to evaluate detected noisy labels. The classification was assessed on CXR14 with area under the receiver operating characteristic curve (AUC). Report from the radiologists indicated that detected noisy labels had high possibility to be true positives. A notable improvement from baseline in performance of classification was observed with the ensemble learning framework. After removing the CXRs with detected noisy labels, 8 out of 14 abnormalities improved significantly on CXR14. The suggested framework achieved AUC score of 0.827 on CXR14. The methods of this study boost the classification on CXR with awareness of the label noise. Expanded experimental results show that all of them were able to improve multi-label thoracic abnormality classification performance, respectively. A new state-of-the-art is achieved in this study.