A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

Seohyun Lee; Hyuno Kim; Hideo Higuchi; Masatoshi Ishikawa; Ryuichiro Natato

doi:10.1109/ICAIIC54071.2022.9722664

A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

Seohyun Lee, Hyuno Kim, Hideo Higuchi, Masatoshi Ishikawa, Ryuichiro Natato

Tokyo University of Science

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

Abstract

The shapes of metastatic cancer cells are considered to be relatively different from non-metastatic cancer cells, especially regarding the degree of development of lamellipodia or the pattern of internal organ arrangement. However, understanding the specific pattern of the metastatic cancer cell has just started to emerge. In this paper, based on the generative adversarial network approach, we attempted to generate metastatic cancer cell images using human breast cancer cells where the metastasis-promoting protein, PAR1, is expressed.

Original language	English
Title of host publication	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	403-406
Number of pages	4
ISBN (Electronic)	9781665458184
DOIs	https://doi.org/10.1109/ICAIIC54071.2022.9722664
Publication status	Published - 2022
Event	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of Duration: 21 Feb 2022 → 24 Feb 2022

Publication series

Name	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

Conference

Conference	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022
Country/Territory	Korea, Republic of
City	Jeju lsland
Period	21/02/22 → 24/02/22

Keywords

biomedical image analysis
breast cancer cell
cell image classification
deep learning
generative adversarial network

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10.1109/ICAIIC54071.2022.9722664

Cite this

Lee, S., Kim, H., Higuchi, H., Ishikawa, M., & Natato, R. (2022). A Generative Adversarial Network Approach to Metastatic Cancer Cell Images. In 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings (pp. 403-406). (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC54071.2022.9722664

Lee, Seohyun ; Kim, Hyuno ; Higuchi, Hideo ; Ishikawa, Masatoshi ; Natato, Ryuichiro. / A Generative Adversarial Network Approach to Metastatic Cancer Cell Images. 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 403-406 (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings).

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title = "A Generative Adversarial Network Approach to Metastatic Cancer Cell Images",

abstract = "The shapes of metastatic cancer cells are considered to be relatively different from non-metastatic cancer cells, especially regarding the degree of development of lamellipodia or the pattern of internal organ arrangement. However, understanding the specific pattern of the metastatic cancer cell has just started to emerge. In this paper, based on the generative adversarial network approach, we attempted to generate metastatic cancer cell images using human breast cancer cells where the metastasis-promoting protein, PAR1, is expressed. ",

keywords = "biomedical image analysis, breast cancer cell, cell image classification, deep learning, generative adversarial network",

author = "Seohyun Lee and Hyuno Kim and Hideo Higuchi and Masatoshi Ishikawa and Ryuichiro Natato",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 ; Conference date: 21-02-2022 Through 24-02-2022",

year = "2022",

doi = "10.1109/ICAIIC54071.2022.9722664",

language = "English",

series = "4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings",

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

pages = "403--406",

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Lee, S, Kim, H, Higuchi, H, Ishikawa, M & Natato, R 2022, A Generative Adversarial Network Approach to Metastatic Cancer Cell Images. in 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 403-406, 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022, Jeju lsland, Korea, Republic of, 21/02/22. https://doi.org/10.1109/ICAIIC54071.2022.9722664

A Generative Adversarial Network Approach to Metastatic Cancer Cell Images. / Lee, Seohyun; Kim, Hyuno; Higuchi, Hideo; Ishikawa, Masatoshi; Natato, Ryuichiro.

4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 403-406 (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings).

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

TY - GEN

T1 - A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

AU - Lee, Seohyun

AU - Kim, Hyuno

AU - Higuchi, Hideo

AU - Ishikawa, Masatoshi

AU - Natato, Ryuichiro

PY - 2022

Y1 - 2022

N2 - The shapes of metastatic cancer cells are considered to be relatively different from non-metastatic cancer cells, especially regarding the degree of development of lamellipodia or the pattern of internal organ arrangement. However, understanding the specific pattern of the metastatic cancer cell has just started to emerge. In this paper, based on the generative adversarial network approach, we attempted to generate metastatic cancer cell images using human breast cancer cells where the metastasis-promoting protein, PAR1, is expressed.

AB - The shapes of metastatic cancer cells are considered to be relatively different from non-metastatic cancer cells, especially regarding the degree of development of lamellipodia or the pattern of internal organ arrangement. However, understanding the specific pattern of the metastatic cancer cell has just started to emerge. In this paper, based on the generative adversarial network approach, we attempted to generate metastatic cancer cell images using human breast cancer cells where the metastasis-promoting protein, PAR1, is expressed.

KW - biomedical image analysis

KW - breast cancer cell

KW - cell image classification

KW - deep learning

KW - generative adversarial network

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U2 - 10.1109/ICAIIC54071.2022.9722664

DO - 10.1109/ICAIIC54071.2022.9722664

M3 - Conference contribution

AN - SCOPUS:85127684940

T3 - 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

SP - 403

EP - 406

BT - 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

Y2 - 21 February 2022 through 24 February 2022

ER -

Lee S, Kim H, Higuchi H, Ishikawa M, Natato R. A Generative Adversarial Network Approach to Metastatic Cancer Cell Images. In 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 403-406. (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings). https://doi.org/10.1109/ICAIIC54071.2022.9722664

A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

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