Journal of Advanced Computational Intelligence and Intelligent Informatics
Online ISSN : 1883-8014
Print ISSN : 1343-0130
ISSN-L : 1883-8014
Regular Papers
Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells
Saori AidaHiroyuki KamedaSakae NishisakoTomonari KasaiAtsushi SatoTomoyasu Sugiyama
ジャーナル 認証あり

2020 年 24 巻 1 号 p. 134-141


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.



© 2020 Fuji Technology Press Ltd.
前の記事 次の記事