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
Published inAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
© 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.
Parent publication ISBN978-1-7281-1991-5
Is part of publicationEMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Programme, AoF
Additional information about fundingThis research was partly funded by Academy of Finland (grant: 314519).
Showing items with similar title or keywords.
Pölönen, Ilkka; Rahkonen, Samuli; Annala, Leevi; Neittaanmäki, Noora (SPIE, The International Society for Optical Engineering, 2019)Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive ...
Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include ...
Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti (Springer, 2020)In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. ...
Rahkonen, Samuli; Koskinen, Emilia; Pölönen, Ilkka; Heinonen, Tuula; Ylikomi, Timo; Äyrämö, Sami; Eskelinen, Matti A. (SPIE, 2020)New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient ...
Pölönen, Ilkka (University of Jyväskylä, 2013)