IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation

• URL: http://arxiv.org/abs/2410.16945v1
• Date: Tue, 22 Oct 2024 12:20:15 GMT
• Title: IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation
• Authors: Junyeong Maeng, Kwanseok Oh, Wonsik Jung, Heung-Il Suk,
• Abstract summary: Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. We propose a novel architecture that employs disentangled representation learning for identity-preserved brain age transformation called IdenBAT. Our method adeptly converts input images to target age while retaining individual characteristics accurately.
• Score: 9.23090816270662
• License:
• Abstract: 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.

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