6533b855fe1ef96bd12b0971
RESEARCH PRODUCT
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
Ilkka PölönenJohn PaoliNoora NeittaanmäkiOscar ZaarLeevi Annalasubject
Imagery PsychotherapySkin NeoplasmsComputer science0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologygenerative adversarial neural networksneuroverkotMachine learningcomputer.software_genre030218 nuclear medicine & medical imagingMachine Learningihosyöpä03 medical and health sciencesAdversarial system0302 clinical medicineHumansLearningReinforcement learning021101 geological & geomatics engineeringArtificial neural networkskin cancerbusiness.industryspektrikuvausHyperspectral imagingComputingMethodologies_PATTERNRECOGNITIONkuvantaminenNeural Networks ComputerArtificial intelligencebusinesscomputerGenerative grammarGenerator (mathematics)description
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. peerReviewed
year | journal | country | edition | language |
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2020-10-06 |