ReBA-Pred-Net：基于MRI的弱监督区域脑年龄预测
ReBA-Pred-Net: Weakly-Supervised Regional Brain Age Prediction on MRI
AI总结：
本文提出ReBA-Pred-Net网络，通过教师-学生框架实现基于MRI的弱监督区域脑年龄预测，引入健康对照相似性和神经疾病相关性指标评估模型有效性。
摘要：
脑年龄已成为衡量脑健康的重要生物标志物。然而，大多数先前工作针对全脑年龄（WBA），这是一种粗略的范式，难以支持疾病表征和发育与衰老模式的研究，因为相关变化通常是区域选择性的而非全脑范围的。因此，鲁棒的区域脑年龄（ReBA）估计至关重要，但尚未建立广泛通用的模型。在本文中，我们提出了区域脑年龄预测网络（ReBA-Pred-Net），一种用于细粒度脑年龄估计的教师-学生框架。教师生成软ReBA以指导学生生成可靠的ReBA估计，通过临床先验一致性约束（同一功能区域内的变化应相似）。为严格评估，我们引入了两个间接指标：健康对照相似性（HCS），通过测试训练和未见健康对照之间的区域脑年龄差距（ReBA减去年龄）分布的一致性来评估统计一致性；以及神经疾病相关性（NDC），通过检查临床确认的患者是否在疾病相关区域表现出升高的脑年龄差距来评估事实一致性。在多个骨干网络上的实验验证了我们方法的统计和事实有效性。
英文摘要：
Brain age has become a prominent biomarker of brain health. Yet most prior work targets whole brain age (WBA), a coarse paradigm that struggles to support tasks such as disease characterization and research on development and aging patterns, because relevant changes are typically region-selective rather than brain-wide. Therefore, robust regional brain age (ReBA) estimation is critical, yet a widely generalizable model has yet to be established. In this paper, we propose the Regional Brain Age Prediction Network (ReBA-Pred-Net), a Teacher-Student framework designed for fine-grained brain age estimation. The Teacher produces soft ReBA to guide the Student to yield reliable ReBA estimates with a clinical-prior consistency constraint (regions within the same function should change similarly). For rigorous evaluation, we introduce two indirect metrics: Healthy Control Similarity (HCS), which assesses statistical consistency by testing whether regional brain-age-gap (ReBA minus chronological age) distributions align between training and unseen HC; and Neuro Disease Correlation (NDC), which assesses factual consistency by checking whether clinically confirmed patients show elevated brain-age-gap in disease-associated regions. Experiments across multiple backbones demonstrate the statistical and factual validity of our method.
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。
