IdenBAT：用于身份保持的脑龄变换的解耦表示学习
IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation
AI总结：
IdenBAT通过解耦表示学习将脑图像中的年龄相关特征与个体身份特征分离，在2D和3D脑数据集上实现了保持个体身份的脑龄变换，性能优于现有方法。
摘要：
脑龄变换旨在将参考脑图像转换为能够准确反映目标年龄组年龄特异性特征的合成图像。该任务的主要目标是仅修改参考图像中与年龄相关的属性，同时保留所有其他与年龄无关的属性。然而，由于骨干编码器提取的特征中各种图像属性固有地纠缠在一起，导致在图像生成过程中同时发生改变，实现这一目标面临重大挑战。为应对这一挑战，我们提出了一种新颖的架构，采用解耦表示学习进行身份保持的脑龄变换，称为IdenBAT。该方法促进了图像特征的分解，确保保留个体特征，同时选择性地变换与年龄相关的特征以匹配目标年龄组的特征。通过在2D和全尺寸3D脑数据集上进行的综合实验，我们的方法能够熟练地将输入图像转换到目标年龄，同时准确保留个体特征。此外，我们的方法在性能保真度方面表现出优于现有最先进方法的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
