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Forecasting Alzheimer’s Disease Progression via Identity-preserved Denoising Diffusion Generative Adversarial Network

2025-11-28

Abstract excerpt

<title>Abstract</title> <p>Forecasting the progression of Alzheimer’s disease (AD) is essential for evaluating secondary prevention measures thought to modify the disease trajectory. However, accurate prediction of longitudinal MRIs remains challenging, particularly in preserving subject identity, as deep generative models may potentially generate plausible future MRIs of different individuals from a single basel...

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Literature Corpus work: d2b7767c-6c8a-5ec5-9c8f-04af5e3f4fa3
DOI: 10.21203/rs.3.rs-7846693/v1

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