Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets

TL;DR

This paper introduces a novel dual-stream model called GDSM for brain age estimation that performs well on small datasets, reducing the need for large data and computational resources while maintaining accuracy.

Contribution

The paper presents a new slice-based dual-stream approach that effectively estimates brain age using limited datasets and fewer resources, addressing key challenges in medical image analysis.

Findings

Achieved MAE of 3.25 years on IBID dataset with 289 subjects.

Demonstrated robustness with an MAE of 4.18 years on the IXI dataset.

Comparable performance to state-of-the-art methods with efficient computation.

Abstract

Brain age estimation involves predicting the biological age of individuals from their brain images, which offers valuable insights into the aging process and the progression of neurodegenerative diseases. Conducting large-scale datasets for medical image analysis is a challenging and time-consuming task. Existing approaches mostly depend on large datasets, which are hard to come by and expensive. These approaches also require sophisticated, resource-intensive models with a large number of parameters, necessitating a considerable amount of processing power. As a result, there is a vital need to develop innovative methods that can achieve robust performance with limited datasets and efficient use of computational resources. This paper proposes a novel slice-based dual-stream method called GDSM (Greedy Dual-Stream Model) for brain age estimation. This method addresses the limitations of large dataset requirements and computational resource intensiveness. The proposed method incorporates local and global aspects of the brain, thereby refining the focus on specific target regions. The approach employs four backbones to predict ages based on local and global features, complemented by a final model for age correction. Our method demonstrates a Mean Absolute Error (MAE) of 3.25 years on the test set of IBID, which only contains 289 subjects. To demonstrate the robustness of our approach for any small dataset, we analyzed the proposed method with the IXI dataset and achieved an MAE of 4.18 years on the test set of IXI. By leveraging dual-stream and greedy strategies, this approach achieves efficiency and robust performance, making it comparable with other state-of-the-art methods. The code for the GDSM model is available at https://github.com/iman2693/GDSM.