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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.

Leevi Annala, Noora Neittaanmaki, John Paoli

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study demonstrates using generative adversarial neural networks to create synthetic hyperspectral skin cancer images. This technique can augment real medical imaging datasets for improved analysis.

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    Area of Science:

    • Medical imaging
    • Artificial intelligence
    • Dermatology

    Background:

    • Hyperspectral imaging provides detailed spectral information crucial for diagnosing skin cancer.
    • Limited availability of diverse hyperspectral skin cancer datasets hinders model training and validation.
    • Generative Adversarial Neural Networks (GANs) show potential for data augmentation in various fields.

    Purpose of the Study:

    • To develop a proof of concept for generating synthetic hyperspectral skin cancer imagery using GANs.
    • To explore the feasibility of GANs for augmenting medical imaging datasets in dermatology.
    • To create a generator capable of producing realistic hyperspectral images of skin cancer.

    Main Methods:

    • Implementation of a Generative Adversarial Neural Network (GAN) framework.
    • Training the discriminator network using real hyperspectral images from skin cancer patients.
    • Developing a generator network to produce artificial hyperspectral skin cancer imagery.

    Main Results:

    • Successful proof of concept for GAN-based hyperspectral skin cancer image generation.
    • Demonstrated ability of the generator to produce data similar to real measurements.
    • Established a foundation for using GANs to augment hyperspectral medical image datasets.

    Conclusions:

    • GANs offer a viable approach for generating synthetic hyperspectral skin cancer images.
    • This method can potentially address data scarcity issues in medical imaging research.
    • Further research can optimize GAN architectures for enhanced realism and clinical utility.