A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

Jialun Wu; Ruonan Zhang; Tieliang Gong; Xinrui Bao; Zeyu Gao; Haichuan Zhang; Chunbao Wang; Chen Li

doi:10.1109/BIBM52615.2021.9669870

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

Jialun Wu
, Ruonan Zhang
, Tieliang Gong
, Xinrui Bao
, Zeyu Gao
, Haichuan Zhang
, Chunbao Wang
, Chen Li

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
Pennsylvania State University
The First Affiliated Hospital of Xi’an Jiaotong University

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

16 Scopus citations

Abstract

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2104-2111
Number of pages	8
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669870
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 9 Dec 2021 → 12 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	9/12/21 → 12/12/21

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1109/BIBM52615.2021.9669870

Cite this

Wu, J., Zhang, R., Gong, T., Bao, X., Gao, Z., Zhang, H., Wang, C., & Li, C. (2021). A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 2104-2111). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669870

Wu, Jialun ; Zhang, Ruonan ; Gong, Tieliang et al. / A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2104-2111 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{52208d15a05c4139ae44e9a9b3288374,

title = "A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning",

abstract = "Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading.",

author = "Jialun Wu and Ruonan Zhang and Tieliang Gong and Xinrui Bao and Zeyu Gao and Haichuan Zhang and Chunbao Wang and Chen Li",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669870",

language = "英语",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2104--2111",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

Wu, J, Zhang, R, Gong, T, Bao, X, Gao, Z, Zhang, H, Wang, C & Li, C 2021, A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2104-2111, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669870

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning. / Wu, Jialun; Zhang, Ruonan; Gong, Tieliang et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2104-2111 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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

TY - GEN

T1 - A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

AU - Wu, Jialun

AU - Zhang, Ruonan

AU - Gong, Tieliang

AU - Bao, Xinrui

AU - Gao, Zeyu

AU - Zhang, Haichuan

AU - Wang, Chunbao

AU - Li, Chen

PY - 2021

Y1 - 2021

N2 - Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading.

AB - Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading.

UR - https://www.scopus.com/pages/publications/85125188188

U2 - 10.1109/BIBM52615.2021.9669870

DO - 10.1109/BIBM52615.2021.9669870

M3 - 会议稿件

AN - SCOPUS:85125188188

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 2104

EP - 2111

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Y2 - 9 December 2021 through 12 December 2021

ER -

Wu J, Zhang R, Gong T, Bao X, Gao Z, Zhang H et al. A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2104-2111. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669870

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

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