Mingyan Yang1, Hisashi Tanaka2, Takayuki Ishida3. 1. Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7 yamadaoka, suita, Osaka, 565-0871, Japan. 2. Division of Health Sciences, Osaka University, 1-7 yamadaoka, suita, Osaka, 565-0871, Japan. 3. Division of Health Sciences, Osaka University, 1-7 yamadaoka, suita, Osaka, 565-0871, Japan. tishida@sahs.med.osaka-u.ac.jp.
Abstract
PURPOSE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.
Authors: Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud Arindra Adiyoso Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A W M van der Laak; Bram van Ginneken; Clara I Sánchez Journal: Med Image Anal Date: 2017-07-26 Impact factor: 8.545