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Age-related morphological brain changes are known to be different in healthy and disease-affected aging. Biological brain age estimation from MRI scans is a common way to quantify this effect whereas differences between biological and chronological age indicate degenerative processes. The ability to visualize and analyze the morphological age-related changes in the image space directly is essential to improve the understanding of brain aging. In this work, we propose a novel deep learning based approach to unify biological brain age estimation and age-conditioned template creation in a single, consistent model. We achieve this by developing a deterministic autoencoder that successfully disentangles age-related morphological changes and subject-specific variations. This allows its use as a brain age regressor as well as a generative brain aging model. The proposed approach demonstrates accurate biological brain age prediction, and realistic generation of age-conditioned brain templates and simulated age-specific brain images when applied to a database of more than 2000 subjects.
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Friday 9th July
L4-9 (short): Detection and Diagnosis 2 - 16:45 - 17:30 (UTC+2)
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