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^*

^*此作品的通讯作者

信息与电子学院

Beijing University of Chemical Technology
China-Japan Friendship Hospital

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

4 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II
编辑	Zhouchen Lin, Liang Wang, Tieniu Tan, Jian Yang, Guangming Shi, Nanning Zheng, Xilin Chen, Yanning Zhang
出版商	Springer
页	173-184
页数	12
ISBN（印刷版）	9783030317225
DOI	https://doi.org/10.1007/978-3-030-31723-2_15
出版状态	已出版 - 2019
活动	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 - Xi'an, 中国期限: 8 11月 2019 → 11 11月 2019

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	11858 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
国家/地区	中国
市	Xi'an
时期	8/11/19 → 11/11/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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

Wei, Xueling ; Tu, Tianqi ; Zhang, Nianrong 等. / Membranous nephropathy identification using hyperspectral microscopic images. Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. 编辑 / Zhouchen Lin ; Liang Wang ; Tieniu Tan ; Jian Yang ; Guangming Shi ; Nanning Zheng ; Xilin Chen ; Yanning Zhang. Springer, 2019. 页码 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. 在 Z Lin, L Wang, T Tan, J Yang, G Shi, N Zheng, X Chen & Y Zhang (编辑), 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), 卷 11858 LNCS, Springer, 页码 173-184, 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, Xi'an, 中国, 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 等.
Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. 编辑 / Zhouchen Lin; Liang Wang; Tieniu Tan; Jian Yang; Guangming Shi; Nanning Zheng; Xilin Chen; Yanning Zhang. Springer, 2019. 页码 173-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11858 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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. 在 Lin Z, Wang L, Tan T, Yang J, Shi G, Zheng N, Chen X, Zhang Y, 编辑, Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. Springer. 2019. 页码 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

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此