@inproceedings{9cb9928d8fde466098b70cec38a120b0,
title = "Spatial-spectral density peaks based discriminant projection for classification of membranous nephropathy hyperspectral pathological image",
abstract = "Microscopic hyperspectral imaging has become an emerging technique for various medical applications. However, high dimensionality of hyperspectral image (HSI) makes image processing and extraction of important diagnostic information challenging. In this paper, a novel dimensionality reduction method named spatial-spectral density peaks based discriminant projection (SSDP) is proposed by considering spatial-spectral density distribution characteristics of immune complexes. The proposed SSDP coupled with support vector machine classifier (SVM) yields high-precision automatic diagnosis of membranous nephropathy (MN). Detailed ex-vivo validation of the proposed method demonstrates the potential clinical value of the system in identifying hepatitis B virus-associated membranous nephropathy (HBV-MN) and primary membranous nephropathy (PMN).",
keywords = "Dimensionality reduction, feature extraction, membranous nephropathy diagnosis, microscopic hyperspectral imaging",
author = "Meng Lv and Wei Li and Ran Tao",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.; 6th International Conference on Fuzzy Systems and Data Mining, FSDM 2020 ; Conference date: 13-11-2020 Through 16-11-2020",
year = "2020",
doi = "10.3233/FAIA200696",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "160--167",
editor = "Tallon-Ballesteros, {Antonio J.}",
booktitle = "Frontiers in Artificial Intelligence and Applications",
address = "Netherlands",
}
