A4 Article in conference proceedings
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network (2020)

Annala, Leevi; Neittaanmäki, Noora; Paoli, John; Zaar, Oscar; Pölönen, Ilkka (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, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1600-1603. DOI: 10.1109/EMBC44109.2020.9176292

JYU authors or editors

Annala, Leevi

Pölönen, Ilkka

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: http://doi.org/10.1109/EMBC44109.2020.9176292

Open Access: Publication channel is not openly available

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

Fields of science:

113 Computer and information sciences (Natural sciences)

217 Medical engineering (Engineering and technology)