Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
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
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Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyDate
2020Copyright
© IEEE, 2020
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.
Publisher
IEEEParent publication ISBN
978-1-7281-1991-5Conference
Is part of publication
EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyISSN Search the Publication Forum
2375-7477Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/41828599
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Academy of FinlandFunding program(s)
Academy Programme, AoF
Additional information about funding
This research was partly funded by Academy of Finland (grant: 314519).License
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