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Deep Learning of Cancer Stem Cell Morphology

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Cancer Stem Cells

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2777))

Abstract

Knowledge regarding cancer stem cell (CSC) morphology is limited, and more extensive studies are therefore required. Image recognition technologies using artificial intelligence (AI) require no previous expertise in image annotation. Herein, we describe the construction of AI models that recognize the CSC morphology in cultures and tumor tissues. The visualization of the AI deep learning process enables insight to be obtained regarding unrecognized structures in an image.

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References

  1. Wang J, Guo L-P, Chen L-Z, Zeng Y-X, Lu SH (2007) Identification of cancer stem cell–like side population cells in human nasopharyngeal carcinoma cell line. Cancer Res 67(8):3716–3724. https://doi.org/10.1158/0008-5472.Can-06-4343

    Article  CAS  PubMed  Google Scholar 

  2. Gupta A, Harrison PJ, Wieslander H, Pielawski N, Kartasalo K, Partel G et al (2019) Deep learning in image cytometry: a review. Cytometry A 95(4):366–380. https://doi.org/10.1002/cyto.a.23701

    Article  PubMed  Google Scholar 

  3. Isola P, Zhu J, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. IEEE, Honolulu

    Book  Google Scholar 

  4. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128(2):336–359. https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

  5. Chen L, Kasai T, Li Y, Sugii Y, Jin G, Okada M et al (2012) A model of cancer stem cells derived from mouse induced pluripotent stem cells. PLoS One 7(4):e33544. https://doi.org/10.1371/journal.pone.0033544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133. https://doi.org/10.1007/BF02478259

    Article  Google Scholar 

  7. Rosenblatt F (1957) The perceptron – a perceiving and recognizing automaton. Cornell Aeronautical Laboratory, Ithaca/New York

    Google Scholar 

  8. Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202. https://doi.org/10.1007/BF00344251

    Article  CAS  PubMed  Google Scholar 

  9. Fukushima K (1979) Neocognitron: Neural network model for a mechanism of pattern recognition unaffected by shift in position. IEICI J62-A(10):658–665

    Google Scholar 

  10. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  11. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. Paper presented at advances in neural information processing systems, 2014

    Google Scholar 

  12. Isola P, Zhu J-Y, Zhou T, Efros AA (2018) Image-to-image translation with conditional adversarial networks. arXiv:1611.07004v07003 [cs.CV], https://ui.adsabs.harvard.edu/abs/2016arXiv161107004I

  13. Goodfellow I (2016) NIPS 2016 tutorial: generative adversarial networks. arXiv:1701.00160, https://www.youtube.com/watch?v=HGYYEUSm-0Q

  14. Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks

    Google Scholar 

  15. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784, https://ui.adsabs.harvard.edu/abs/2014arXiv1411.1784M

  16. Aida S, Kameda H, Nishisako S, Kasai T, Sato A, Sugiyama T (2020) Conditional generative adversarial networks to model iPSC-derived cancer stem cells. J Adv Comput Intell Intell Inf 24(1):134–141. https://doi.org/10.20965/jaciii.2020.p0134

    Article  Google Scholar 

  17. Aida S, Okugawa J, Fujisaka S, Kasai T, Kameda H, Sugiyama T (2020) Deep learning of cancer stem cell morphology using conditional generative adversarial networks. Biomol Ther 10(6):931. https://doi.org/10.3390/biom10060931

    Article  CAS  Google Scholar 

  18. Hanai Y, Ishihata H, Zhang Z, Maruyama R, Kasai T, Kameda H et al (2022) Temporal and locational values of images affecting the deep learning of cancer stem cell morphology. Biomedicine 10(5):941. https://doi.org/10.3390/biomedicines10050941

    Article  CAS  Google Scholar 

  19. Simonyan K, Zisserman A (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/abs/1409.1556

  20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image Recognition. pp 770–778

    Google Scholar 

  21. PyTorch installation (2022) https://pytorch.org/get-started/locally/

  22. Gildenblat J (2021) PyTorch library for CAM methods. https://github.com/jacobgil/pytorch-grad-cam

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Correspondence to Tomoyasu Sugiyama .

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Kameda, H., Ishihata, H., Sugiyama, T. (2024). Deep Learning of Cancer Stem Cell Morphology. In: Papaccio, F., Papaccio, G. (eds) Cancer Stem Cells. Methods in Molecular Biology, vol 2777. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3730-2_17

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  • DOI: https://doi.org/10.1007/978-1-0716-3730-2_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3729-6

  • Online ISBN: 978-1-0716-3730-2

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