  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.
