Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease

Sai Pan, Yibing Fu, Pu Chen, Jiaona Liu, Weicen Liu, Xiaofei Wang, Guangyan Cai, Zhong Yin, Jie Wu, Li Tang, Yong Wang, Shuwei Duan, Ning Dai, Lai Jiang, Mai Xu and Xiangmei Chen
Additional contact information
Sai Pan: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Yibing Fu: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Pu Chen: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Jiaona Liu: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Weicen Liu: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Xiaofei Wang: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Guangyan Cai: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Zhong Yin: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Jie Wu: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Li Tang: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Yong Wang: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Shuwei Duan: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China
Ning Dai: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Lai Jiang: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Mai Xu: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Xiangmei Chen: National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Institute of Nephrology of Chinese PLA, Department of Nephrology, General Hospital of Chinese PLA, Medical School of Chinese PLA, Beijing 100853, China

IJERPH, 2021, vol. 18, issue 20, 1-11

Abstract: Chronic kidney disease is one of the most important causes of mortality worldwide, but a shortage of nephrology pathologists has led to delays or errors in its diagnosis and treatment. Immunofluorescence (IF) images of patients with IgA nephropathy (IgAN), membranous nephropathy (MN), diabetic nephropathy (DN), and lupus nephritis (LN) were obtained from the General Hospital of Chinese PLA. The data were divided into training and test data. To simulate the inaccurate focus of the fluorescence microscope, the Gaussian method was employed to blur the IF images. We proposed a novel multi-task learning (MTL) method for image quality assessment, de-blurring, and disease classification tasks. A total of 1608 patients’ IF images were included—1289 in the training set and 319 in the test set. For non-blurred IF images, the classification accuracy of the test set was 0.97, with an AUC of 1.000. For blurred IF images, the proposed MTL method had a higher accuracy (0.94 vs. 0.93, p < 0.01) and higher AUC (0.993 vs. 0.986) than the common MTL method. The novel MTL method not only diagnosed four types of kidney diseases through blurred IF images but also showed good performance in two auxiliary tasks: image quality assessment and de-blurring.

Keywords: multi-task learning; deep learning; immunofluorescence images; kidney (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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