Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
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.
Main Authors
Format
Conferences
Conference paper
Published
2020
Series
Subjects
Publication in research information system
Publisher
IEEE
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202010136188Use this for linking
Parent publication ISBN
978-1-7281-1991-5
Review status
Peer reviewed
ISSN
2375-7477
DOI
https://doi.org/10.1109/EMBC44109.2020.9176292
Conference
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Language
English
Published in
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Is part of publication
EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Citation
- 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
License
Funder(s)
Research Council of Finland
Funding program(s)
Academy Programme, AoF
Akatemiaohjelma, SA
Additional information about funding
This research was partly funded by Academy of Finland (grant: 314519).
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