Important applications of advancements in machine learning, are in the area of healthcare, more so for neurological disorder detection. A crucial step towards understanding the neurological status, is to estimate the brain age using structural MRI volumes, in order to measure its deviation from chronological age. Factors that contribute to brain age are best captured using a data-driven approach, such as deep learning. However, it places a huge demand on the availability of diverse datasets. In this work, we propose a robust brain age estimation paradigm that utilizes a 3D CNN model, by-passing the need for model-retraining across datasets. The proposed model consists of seven 3D CNN layers, with a shared spatial attention layer incorporated at each CNN layer followed by five dense layers. The novelty of the proposed method lies in the idea of spatial attention module, with shared weights across the CNN layers. This weight sharing ensures directed attention to specific brain regions, for localizing age-related features within the data, lending robustness. The proposed model, trained on ADNI dataset comprising 516 T1 weighted MRI volumes of healthy subjects, resulted in Mean Absolute Error (MAE) of 1.662 years, which is an improvement of 1.688 years over the state-of-the-art (SOTA) model, based on disjoint test samples from the same repository. To illustrate generalizability, the same pipeline was utilized on volumes from a publicly available source called OASIS3. From OASIS3, MRI volumes 890 healthy subjects were utilized resulting in MAE of 2.265 years. Due to diversity in acquisitions across multiple sites, races and genetic factors, traditional CNN models are not guaranteed to prioritize brain regions crucial for age estimation. In contrast, the proposed weight-shared spatial attention module, directs attention on specific regions, required for the estimation.

翻译：机器学习进展的重要应用领域在于医疗保健，尤其是神经系统疾病检测。理解神经状态的关键步骤是利用结构MRI体积估计脑龄，以测量其与实足年龄的偏差。采用数据驱动方法（如深度学习）最能捕捉影响脑龄的因素，但这对多样化数据集的可用性提出了极高要求。本研究提出一种稳健的脑龄估计范式，采用3D CNN模型，避免了跨数据集重新训练模型的需求。所提模型包含七个3D CNN层，每个CNN层后接共享空间注意力层，随后连接五个全连接层。该方法的创新性在于空间注意力模块的设计，其权重在CNN层间共享。这种权重共享机制确保了对特定脑区的定向注意力，能够定位数据中与年龄相关的特征，从而增强模型稳健性。使用包含516名健康受试者T1加权MRI体积的ADNI数据集训练模型，在同源数据集的独立测试样本上获得1.662年的平均绝对误差（MAE），较当前最优模型提升1.688年。为验证泛化能力，在公开数据集OASIS3的890名健康受试者MRI体积上进行测试，获得2.265年的MAE。由于多中心采集差异、种族及遗传因素的存在，传统CNN模型难以确保优先处理对年龄估计至关重要的脑区。相比之下，所提出的权重共享空间注意力模块能够将注意力定向聚焦于估计所需的特定区域。