Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells-Reference-Cited by-同舟云学术

Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells

Published:2020-01-20 Issue:1 Volume:24 Page:134-141
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

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

Abstract

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.

Publisher

Fuji Technology Press Ltd.

Subject

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

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