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    <title language="eng">GestaltGAN: Synthetic Photorealistic Portraits of Individuals with Rare Genetic Disorders</title>
    <parentTitle language="eng">European Journal of Human Genetics</parentTitle>
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Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher. However, medical data is often sparsely available, and sharing patient images involves risks related to privacy and re-identification. Therefore, we explored whether generative neural networks can be used to synthesize accurate portraits for rare disorders. We modified a StyleGAN architecture and trained it to produce artificial condition-specific portraits for multiple disorders. In addition, we present a technique that generates a sharp and detailed average patient portrait for a given disorder. We trained our GestaltGAN on the 20 most frequent disorders from the GestaltMatcher database. 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    <author>Aron Kirchhoff</author>
    <author>Alexander Hustinx</author>
    <author>Behnam Javanmardi</author>
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    <title language="eng">GANonymization: A GAN-Based Face Anonymization Framework for Preserving Emotional Expressions</title>
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