IdenBAT: Disentangled representation learning for identity-preserved brain age transformation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces IdenBAT, a new method for brain age transformation that preserves individual identity. It accurately changes brain images to a target age while keeping unique features intact.
Area Of Science
- Neuroimaging
- Artificial Intelligence
- Computer Vision
Background
- Brain age transformation aims to modify brain images to reflect target age-specific features.
- Preserving age-irrelevant attributes during transformation is challenging due to entangled image features.
Purpose Of The Study
- To propose a novel architecture, IdenBAT, for identity-preserved brain age transformation.
- To disentangle image features for selective age-related attribute modification.
Main Methods
- Developed a novel architecture employing disentangled representation learning (IdenBAT).
- Decomposes image features to preserve individual traits while transforming age characteristics.
- Validated on 2D and 3D brain datasets.
Main Results
- IdenBAT accurately converts brain images to target ages while preserving individual characteristics.
- The method demonstrates superior performance fidelity compared to state-of-the-art approaches.
- Successful application on both 2D and 3D neuroimaging data.
Conclusions
- IdenBAT effectively achieves identity-preserved brain age transformation.
- The disentangled representation learning approach overcomes feature entanglement challenges.
- IdenBAT offers a promising solution for realistic brain age manipulation in neuroimaging research.
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