Global-Local Transformer for Brain Age Estimation (2109.01663v1)

Abstract: 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: \url{https://github.com/shengfly/global-local-transformer}.

• The paper introduces a novel global-local transformer architecture that accurately estimates brain age from MRI scans by integrating global context with local details using an attention mechanism.
• The model employs a two-pathway network, one for global MRI information and one for local patch details, which are fused using a transformer-based attention mechanism to optimize prediction accuracy.
• Tested on eight datasets, the model achieved a low mean absolute error of 2.70 years and high correlation (0.9853), demonstrating superior performance and providing interpretable insights into age-predictive brain regions.

Overview of "Global-Local Transformer for Brain Age Estimation"

Introduction

The paper, "Global-Local Transformer for Brain Age Estimation," presents an innovative approach to estimating brain age using magnetic resonance imaging (MRI) in conjunction with deep learning techniques. Specifically, it introduces a novel neural network architecture named the global-local transformer. This method aims to enhance the accuracy of brain age estimation by integrating global contextual data from an entire MRI image with local detailed information from segmented patches of the same image.

Methodology

The proposed approach consists of a two-pathway network: a global-pathway that captures overall contextual information from entire MRI images and a local pathway responsible for extracting fine-grained details from localized patches. These pathways work synergistically within a global-local transformer, utilizing an attention mechanism. Inspired by the transformer architecture commonly employed in natural language processing, this mechanism facilitates the fusion of global and local information to optimize brain age estimation.

Experimental Evaluation

The authors rigorously test their model on a collection of eight public datasets encompassing 8,379 MRI scans, with subjects ranging in age from 0 to 97 years. Among these datasets, six are used for cross-validation purposes, while two are reserved for evaluating the model's generalization capabilities. In comparison to other state-of-the-art methods, the global-local transformer demonstrates significant improvement, reducing the mean absolute error (MAE) for estimated ages to 2.70 years, and achieving a correlation coefficient of 0.9853 between estimated brain ages and chronological ages.

Interpretability and Insights

One of the critical advantages of the global-local transformer is its ability to provide insights into the regions of the brain that are most informative for age estimation. By evaluating local patches, regions contributing the most to age prediction are identified and visualized, enhancing the interpretability of model predictions. This capability also opens pathways for exploring age-related biomarkers across different brain regions, potentially linking these findings to the risk of neurodegenerative diseases.

Comparative Analysis and Results

The authors compare their approach with various baseline models, including common CNN architectures, BagNet methods, and other transformer implementations. They conclude that, despite the complexity added by integrating local patches and attention-based data fusion, the global-local transformer consistently outperforms these alternatives. Furthermore, when assessed against a range of existing deep learning models specifically designed for brain age estimation, the proposed method remains superior, delivering high precision and robust correlation metrics across diverse datasets.

Implications and Future Directions

The implications of this research are profound, particularly in leveraging brain age as a biomarker for neurological health and identifying potential risks for cognitive decline. Looking forward, the authors suggest that this model's integration with clinical practice could aid in detecting neurodegenerative diseases through alterations in brain age. Although initially applied to healthy populations, the adaptability and interpretability of this approach hold promise for broader applications, including pathological MRI datasets. Future work would focus on refining the model’s ability to generalize across different modalities and continue improving prediction accuracy through enhanced data collection and continuous refinement of the underlying architecture.

Conclusion

In sum, the "Global-Local Transformer for Brain Age Estimation" offers a significant advancement in the field of automated age estimation using neuroimaging technologies. By effectively marrying global and local MRI information through a sophisticated attention mechanism, this model sets a new benchmark in precision and interpretability for brain age prediction tasks. The results underscore its potential utility in clinical and research settings, heralding new opportunities to deepen our understanding of brain aging processes and their medical implications.