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

TL;DR

IdenBAT introduces a disentangled representation learning framework for brain age transformation, effectively modifying age-related features while preserving individual identity, demonstrated through superior results on 2D and 3D brain datasets.

Contribution

The paper presents a novel architecture that disentangles features to preserve identity during brain age transformation, addressing attribute entanglement issues in previous methods.

Findings

Effective age-specific feature modification while preserving identity.

Superior performance over existing methods on multiple datasets.

Works on both 2D and 3D brain images.

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.