Conditional generative adversarial networks to model iPSC-derived cancer stem cells

Saori Aida, Hiroyuki Kameda, Sakae Nishisako, Tomonari Kasai, Atsushi Sato, Tomoyasu Sugiyama

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The realization of effective and low-cost drug discovery is imperative to enable people to easily purchase and use medicines when necessary. This paper reports a smart system for detecting iPSC-derived cancer stem cells by using conditional generative adversarial networks. This system with artificial intelligence (AI) accepts a normal image from a microscope and transforms it into a corresponding fluorescent-marked fake image. The AI system learns 10,221 sets of paired pictures as input. Consequently, the system’s performance shows that the correlation between true fluorescent-marked images and fake fluorescent-marked images is at most 0.80. This suggests the fundamental validity and feasibility of our proposed system. Moreover, this research opens a new way for AI-based drug discovery in the process of iPSC-derived cancer stem cell detection.

Original languageEnglish
Pages (from-to)134-141
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number1
Publication statusPublished - 2020
Externally publishedYes


  • Artificial intelligence
  • Conditional generative adversarial networks
  • Drug discovery
  • IPSC-derived cancer stem cells
  • Pix2pix

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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