Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0–97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer .

中文翻译：

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用于脑年龄估计的全局局部变换器

深度学习可以提供基于脑磁共振成像 (MRI) 的快速脑年龄估计。然而，大多数研究使用一个神经网络从整个输入图像中提取全局信息，而忽略了局部细粒度细节。在本文中，我们提出了一个全局-局部变换器，它包括一个从整个输入图像中提取全局上下文信息的全局路径和一个从局部块中提取局部细粒度细节的局部路径。来自局部补丁的细粒度信息通过受转换器启发的注意力机制与全局上下文信息融合，以估计大脑年龄。我们在 8 个公共数据集上评估了所提出的方法，其中包含 8,379 个年龄范围为 0-97 岁的健康大脑 MRI。6 个数据集用于交叉验证，2 个数据集用于评估通用性。与其他最先进的方法相比，所提出的全局-局部变换器将估计年龄的平均绝对误差降低到 2.70 年，并将估计年龄和实际年龄的相关系数增加到 0.9853。此外，我们提出的方法提供了区域信息，哪些局部补丁对大脑年龄估计最有用。我们的源代码可在以下位置获得：我们提出的方法提供了区域信息，其中哪些局部补丁对大脑年龄估计最有用。我们的源代码可在以下位置获得：我们提出的方法提供了区域信息，其中哪些局部补丁对大脑年龄估计最有用。我们的源代码可在以下位置获得：https://github.com/shengfly/global-local-transformer .