Membranous nephropathy identification using hyperspectral microscopic images

Xueling Wei; Tianqi Tu; Nianrong Zhang; Yue Yang; Wenge Li; Wei Li

doi:10.1007/978-3-030-31723-2_15

Membranous nephropathy identification using hyperspectral microscopic images

Xueling Wei
, Tianqi Tu
, Nianrong Zhang
, Yue Yang
, Wenge Li
, Wei Li^*

^*Corresponding author for this work

School of Information and Electronics

Beijing University of Chemical Technology
China-Japan Friendship Hospital

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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 immunofluo-rescence 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.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II
Editors	Zhouchen Lin, Liang Wang, Tieniu Tan, Jian Yang, Guangming Shi, Nanning Zheng, Xilin Chen, Yanning Zhang
Publisher	Springer
Pages	173-184
Number of pages	12
ISBN (Print)	9783030317225
DOIs	https://doi.org/10.1007/978-3-030-31723-2_15
Publication status	Published - 2019
Event	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 - Xi'an, China Duration: 8 Nov 2019 → 11 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11858 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
Country/Territory	China
City	Xi'an
Period	8/11/19 → 11/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Deep neural network
Hyperspectral microscopic images
MN Identification

Access to Document

10.1007/978-3-030-31723-2_15

Cite this

Wei, X., Tu, T., Zhang, N., Yang, Y., Li, W., & Li, W. (2019). Membranous nephropathy identification using hyperspectral microscopic images. In Z. Lin, L. Wang, T. Tan, J. Yang, G. Shi, N. Zheng, X. Chen, & Y. Zhang (Eds.), Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II (pp. 173-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11858 LNCS). Springer. https://doi.org/10.1007/978-3-030-31723-2_15

Wei, Xueling ; Tu, Tianqi ; Zhang, Nianrong et al. / Membranous nephropathy identification using hyperspectral microscopic images. Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. editor / Zhouchen Lin ; Liang Wang ; Tieniu Tan ; Jian Yang ; Guangming Shi ; Nanning Zheng ; Xilin Chen ; Yanning Zhang. Springer, 2019. pp. 173-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{55c27339c51e43549ed1340f96326439,

title = "Membranous nephropathy identification using hyperspectral microscopic images",

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 immunofluo-rescence 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.",

keywords = "Deep neural network, Hyperspectral microscopic images, MN Identification",

author = "Xueling Wei and Tianqi Tu and Nianrong Zhang and Yue Yang and Wenge Li and Wei Li",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 ; Conference date: 08-11-2019 Through 11-11-2019",

year = "2019",

doi = "10.1007/978-3-030-31723-2\_15",

language = "English",

isbn = "9783030317225",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "173--184",

editor = "Zhouchen Lin and Liang Wang and Tieniu Tan and Jian Yang and Guangming Shi and Nanning Zheng and Xilin Chen and Yanning Zhang",

booktitle = "Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II",

address = "Germany",

}

Wei, X, Tu, T, Zhang, N, Yang, Y, Li, W & Li, W 2019, Membranous nephropathy identification using hyperspectral microscopic images. in Z Lin, L Wang, T Tan, J Yang, G Shi, N Zheng, X Chen & Y Zhang (eds), Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11858 LNCS, Springer, pp. 173-184, 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, Xi'an, China, 8/11/19. https://doi.org/10.1007/978-3-030-31723-2_15

Membranous nephropathy identification using hyperspectral microscopic images. / Wei, Xueling; Tu, Tianqi; Zhang, Nianrong et al.
Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. ed. / Zhouchen Lin; Liang Wang; Tieniu Tan; Jian Yang; Guangming Shi; Nanning Zheng; Xilin Chen; Yanning Zhang. Springer, 2019. p. 173-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11858 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Membranous nephropathy identification using hyperspectral microscopic images

AU - Wei, Xueling

AU - Tu, Tianqi

AU - Zhang, Nianrong

AU - Yang, Yue

AU - Li, Wenge

AU - Li, Wei

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - 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 immunofluo-rescence 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.

AB - 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 immunofluo-rescence 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.

KW - Deep neural network

KW - Hyperspectral microscopic images

KW - MN Identification

UR - https://www.scopus.com/pages/publications/85076977476

U2 - 10.1007/978-3-030-31723-2_15

DO - 10.1007/978-3-030-31723-2_15

M3 - Conference contribution

AN - SCOPUS:85076977476

SN - 9783030317225

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 173

EP - 184

BT - Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II

A2 - Lin, Zhouchen

A2 - Wang, Liang

A2 - Tan, Tieniu

A2 - Yang, Jian

A2 - Shi, Guangming

A2 - Zheng, Nanning

A2 - Chen, Xilin

A2 - Zhang, Yanning

PB - Springer

T2 - 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

Y2 - 8 November 2019 through 11 November 2019

ER -

Wei X, Tu T, Zhang N, Yang Y, Li W, Li W. Membranous nephropathy identification using hyperspectral microscopic images. In Lin Z, Wang L, Tan T, Yang J, Shi G, Zheng N, Chen X, Zhang Y, editors, Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. Springer. 2019. p. 173-184. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31723-2_15

Membranous nephropathy identification using hyperspectral microscopic images

Abstract

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UN SDGs

Keywords

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