A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publication4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages403-406
Number of pages4
ISBN (Electronic)9781665458184
DOIs
Publication statusPublished - 2022
Event4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of
Duration: 21 Feb 202224 Feb 2022

Publication series

Name4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

Conference

Conference4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022
Country/TerritoryKorea, Republic of
CityJeju lsland
Period21/02/2224/02/22

Keywords

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

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