Abstract
In clinical diagnosis of membranous nephropathy (MN), separating hepatitis B virus-associated membranous nephropathy (HBV-MN) and primary membranous nephropathy (PMN) is an important step. Currently, most diagnostic technique is to conduct immunofluorescence on kidney biopsy samples with high false positive probability. In this paper, an automatic MN identification approach using medical hyperspectral microscopic images is developed. The proposed framework, denoted as local fisher discriminant analysis-deep neural network (LFDA-DNN), firstly constructs a subspace with well separability for HBV-MN and PMN through projection, and then obtains high-level features that are beneficial for final classification via a DNN-based network. To evaluate the effectiveness of LFDA-DNN, experiments are implemented on a real MN dataset, and the results confirm the superiority of LFDA-DNN for recognising HBV-MN and PMN precisely.
This work was supported by Beijing Natural Science Foundation (4172043), Beijing Nova Program (Z171100001117050), and in part by the Research Fund for Basic Researches in Central Universities under Grant PYBZ1831.
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Wei, X., Tu, T., Zhang, N., Yang, Y., Li, W., Li, W. (2019). Membranous Nephropathy Identification Using Hyperspectral Microscopic Images. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_15
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