Global-Local Transformer for Brain Age Estimation.
Global-Local Transformer for Brain Age Estimation.
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DOI：
10.1109/tmi.2021.3108910
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发表时间：
2022-01
影响因子：
10.6
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中科院分区：
文献类型：
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深度学习可以提供基于脑磁共振成像（MRI）的快速大脑年龄估计。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，忽略了局部细粒度的细节。在本文中，我们提出了一个全局-局部Transformer，它包括一个全局路径，从整个输入图像中提取全局上下文信息和一个局部路径，从局部补丁中提取局部细粒度的细节。受Transformer的启发，通过注意机制将局部块的细粒度信息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岁的健康脑MRI。6个数据集用于交叉验证，2个数据集用于评价通用性。与现有方法相比，全局-局部Transformer方法将年龄估计的平均绝对误差降低到2.70年，将年龄估计与实际年龄的相关系数提高到0.9853。此外，我们提出的方法提供了区域信息，其中局部补丁是最翔实的大脑年龄估计。我们的源代码可以在https://github.com/shengfly/global-local-transformer上找到。
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.