IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation
This addresses the challenge of disentangling age and identity features in brain imaging for medical applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of brain age transformation by modifying age-related attributes while preserving individual identity, achieving superior performance fidelity over existing state-of-the-art methods in experiments on 2D and 3D brain datasets.
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the reference image while preserving all other age-irrelevant attributes. However, achieving this goal poses substantial challenges due to the inherent entanglement of various image attributes within features extracted from a backbone encoder, resulting in simultaneous alterations during the image generation. To address this challenge, we propose a novel architecture that employs disentangled representation learning for identity-preserved brain age transformation called IdenBAT. This approach facilitates the decomposition of image features, ensuring the preservation of individual traits while selectively transforming age-related characteristics to match those of the target age group. Through comprehensive experiments conducted on both 2D and full-size 3D brain datasets, our method adeptly converts input images to target age while retaining individual characteristics accurately. Furthermore, our approach demonstrates superiority over existing state-of-the-art regarding performance fidelity.