Top > JACIII > Conditional Generative Adversarial Networks to Model iPSC-Deri ...

single-jc.php

JACIII Vol.24 No.1 pp. 134-141
doi: 10.20965/jaciii.2020.p0134
(2020)

Paper:

Views over last 60 days: 1,021

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

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

*School of Computer Science, Tokyo University of Technology
1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan

**Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology
1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan

***School of Bioscience and Biotechnology, Tokyo University of Technology
1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan

Received:
February 21, 2019
Accepted:
October 28, 2019
Published:
January 20, 2020
Creative Commons License
Keywords:
conditional generative adversarial networks, pix2pix, iPSC-derived cancer stem cells, drug discovery, artificial intelligence
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.

Cite this article as:
S. Aida, H. Kameda, S. Nishisako, T. Kasai, A. Sato, and T. Sugiyama, “Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.1, pp. 134-141, 2020.
Data files:
References
  1. [1] R. Yu, X. Xu, and Z. Wang, “Influence of Object Detection in Deep Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 683-688, 2018.
  2. [2] W. Liu, Y. Yang, and L. Wei, “Weather Recognition of Street Scene Based on Sparse Deep Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.3, pp. 403-408, 2017.
  3. [3] M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” arXiv preprint arXiv: 1411.1784, 2014.
  4. [4] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Nets,” arXiv: 1406.2661 [stat.ML], 2014.
  5. [5] A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” arXiv preprint arXiv: 1511.06434, 2015.
  6. [6] C. H. Lin, E. Yumer, O. Wang, E. Shechtman, and S. Lucey, “ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 9455-9464, 2018.
  7. [7] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 2242-2251, 2017.
  8. [8] Y. Choi et al., “StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 8789-8797, 2018.
  9. [9] T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” arXiv preprint arXiv:1710.10196, 2017.
  10. [10] A. Odena, C. Olah, and J. Shlens, “Conditional Image Synthesis with Auxiliary Classifier Gans,” Proc. of the 34th Int. Conf. on Machine Learning, Vol.70, pp. 2642-2651, 2017.
  11. [11] H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-Attention Generative Adversarial Networks,” arXiv preprint arXiv: 1805.08318, 2018.
  12. [12] X. Chen et al., “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets,” Advances in Neural Information Processing Systems, pp. 2172-2180, 2016.
  13. [13] H. Zhang et al., “StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 5908-5916, 2017.
  14. [14] T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” Int. Conf. on Information Processing in Medical Imaging, pp. 146-157, 2017.
  15. [15] N. Gong, Y. Yang, Y. Liu, and D. Liu, “Dynamic Facial Expression Synthesis Driven by Deformable Semantic Parts,” Proc. of 24th Int. Conf. on Pattern Recognition (ICPR), pp. 2929-2934, doi: 10.1109/ICPR.2018.8545831, 2018.
  16. [16] G. Keskin, T. Lee, C. Stephenson, and O. H. Elibol, “Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems,” arXiv: 1905.12531 [eess.AS], 2019.
  17. [17] J. Yang, T. Li, G. Liang, W. He, and Y. Zhao, “A Simple Recurrent Unit Model based Intrusion Detection System with DCGAN,” IEEE Access, Vol.7, pp. 83286-83296, doi: 10.1109/ACCESS.2019.2922692 (in print).
  18. [18] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1125-1134, 2017.
  19. [19] International Human Genome Sequencing Consortium, “Initial sequencing and analysis of the human genome,” Nature, Vol.409, pp. 860-921, 2001.
  20. [20] J. C. Venter, M. D. Adams et al., “The Sequence of the Human Genome,” Science, Vol.291, No.5507, pp. 1304-1351, 2011.
  21. [21] M. Prieto-Vila, T. Yan, A. S. Calle, N. Nair, L. Hurtley, T. Kasai, H. Kakuta, J. Masuda, H. Murakami, A. Mizutani, and M. Seno, “iPSC-derived cancer stem cells provide a model of tumor vasculature,” American J. of Cancer Research, Vol.6, No.9, pp. 1906-1921, 2016.
  22. [22] H. Kameda, S. Aida, S. Nishisako, T. Kasai, A. Sato, and T. Sugiyama, “Implementation and its Evaluation of a Prototype System to detect iPSC-derived Cancer Stem Cell,” The 1st Annual Meeting of the Japanese Association for Medical Artificial Intelligence, G-32, pp. 85, 2019.
  23. [23] S. Nishisako, S. Aida, H. Kameda, T. Kasai, A. Sato, and T. Sugiyama, “Deep-learning of cancer stem cell morphology for anti-cancer stem cell molecule screening,” Chem-Bio Informatics (CBI) Society Annual Meeting 2018, p. 85, 2018.
  24. [24] S. Russell and P. Norvig, “Artificial Intelligence – A Modern Approach –,” 2nd Edition, Prentice Hall, 2003.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 18, 2024