Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Diseases related progress simulated through latent space image manipulation is difficult to interpret.
Goal(s): The goal was to develop an approach allowing for an improved interpretation of latent space image manipulation.
Approach: A StyleGAN model trained on MRI data from MS patients and healthy controls was used for image manipulation. The direction in latent space for generating images mimicking diseases progression towards MS was determined. The spatial changes were analyzed through eigenvalue decomposition.
Results: The decomposition approach revealed a pattern resembling a polynomial series, suggesting a parameterized data manipulation, with the second component being the most informative for illustrating disease related image changes.
Impact: The analysis method for disentanglement complex image changes through latent space manipulation offers improved predictive accuracy and enhances our understanding of disease progression in neuroimaging research by isolating disease-related image features with a parameter-free approach.
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