A4 Article in conference proceedings
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network (2020)
Annala, L., Neittaanmäki, N., Paoli, J., Zaar, O., & Pölönen, I. (2020). Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network. In EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1600-1603). IEEE. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC44109.2020.9176292
JYU authors or editors
Publication details
All authors or editors: Annala, Leevi; Neittaanmäki, Noora; Paoli, John; Zaar, Oscar; Pölönen, Ilkka
Parent publication: EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Place and date of conference: Montreal, QC, Canada, 20.-24.7.2020
ISBN: 978-1-7281-1991-5
eISBN: 978-1-7281-1990-8
Journal or series: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ISSN: 2375-7477
eISSN: 1557-170X
Publication year: 2020
Pages range: 1600-1603
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/EMBC44109.2020.9176292
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72130
Abstract
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.
Keywords: spectral imaging; imaging; skin cancer; neural networks (information technology)
Free keywords: generative adversarial neural networks; skin cancer
Contributing organizations
Related projects
- SPECTRAL IMAGING OF COMPLEX SURFACE TOMOGRAPHIES
- Pölönen, Ilkka
- Academy of Finland
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1