Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning

Emilio Garcia; Renato Hermoza; Cesar Beltran Castanon; Luis Cano; Miluska Castillo; Carlos Castanneda

doi:10.1109/CBMS.2017.94

Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning

Emilio Garcia, Renato Hermoza, Cesar Beltran Castanon, Luis Cano, Miluska Castillo, Carlos Castanneda

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

58 Scopus citations

Abstract

Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017
Editors	Panagiotis D. Bamidis, Stathis Th. Konstantinidis, Pedro Pereira Rodrigues
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	200-204
Number of pages	5
ISBN (Electronic)	9781538617106
DOIs	https://doi.org/10.1109/CBMS.2017.94
State	Published - 10 Nov 2017
Event	30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece Duration: 22 Jun 2017 → 24 Jun 2017

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2017-June
ISSN (Print)	1063-7125

Conference

Conference	30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017
Country/Territory	Greece
City	Thessaloniki
Period	22/06/17 → 24/06/17

Keywords

cell detection
deep learning
gastric cancer
immunohistochemistry

UN SDGs

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

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10.1109/CBMS.2017.94

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Garcia, E., Hermoza, R., Castanon, C. B., Cano, L., Castillo, M., & Castanneda, C. (2017). Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning. In P. D. Bamidis, S. T. Konstantinidis, & P. P. Rodrigues (Eds.), Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017 (pp. 200-204). Article 8104187 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2017-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2017.94

Garcia, Emilio ; Hermoza, Renato ; Castanon, Cesar Beltran et al. / Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning. Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017. editor / Panagiotis D. Bamidis ; Stathis Th. Konstantinidis ; Pedro Pereira Rodrigues. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 200-204 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

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title = "Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning",

abstract = "Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.",

keywords = "cell detection, deep learning, gastric cancer, immunohistochemistry",

author = "Emilio Garcia and Renato Hermoza and Castanon, {Cesar Beltran} and Luis Cano and Miluska Castillo and Carlos Castanneda",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 ; Conference date: 22-06-2017 Through 24-06-2017",

year = "2017",

month = nov,

day = "10",

doi = "10.1109/CBMS.2017.94",

language = "English",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

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

pages = "200--204",

editor = "Bamidis, {Panagiotis D.} and Konstantinidis, {Stathis Th.} and Rodrigues, {Pedro Pereira}",

booktitle = "Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017",

}

Garcia, E, Hermoza, R, Castanon, CB, Cano, L, Castillo, M & Castanneda, C 2017, Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning. in PD Bamidis, ST Konstantinidis & PP Rodrigues (eds), Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017., 8104187, Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2017-June, Institute of Electrical and Electronics Engineers Inc., pp. 200-204, 30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017, Thessaloniki, Greece, 22/06/17. https://doi.org/10.1109/CBMS.2017.94

Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning. / Garcia, Emilio; Hermoza, Renato; Castanon, Cesar Beltran et al.
Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017. ed. / Panagiotis D. Bamidis; Stathis Th. Konstantinidis; Pedro Pereira Rodrigues. Institute of Electrical and Electronics Engineers Inc., 2017. p. 200-204 8104187 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2017-June).

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

TY - GEN

T1 - Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning

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AU - Hermoza, Renato

AU - Castanon, Cesar Beltran

AU - Cano, Luis

AU - Castillo, Miluska

AU - Castanneda, Carlos

PY - 2017/11/10

Y1 - 2017/11/10

N2 - Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.

AB - Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.

KW - cell detection

KW - deep learning

KW - gastric cancer

KW - immunohistochemistry

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M3 - Conference contribution

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BT - Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017

A2 - Bamidis, Panagiotis D.

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Garcia E, Hermoza R, Castanon CB, Cano L, Castillo M, Castanneda C. Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning. In Bamidis PD, Konstantinidis ST, Rodrigues PP, editors, Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 200-204. 8104187. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). doi: 10.1109/CBMS.2017.94

Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning

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Keywords

UN SDGs

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