MFCA: Collaborative prediction algorithm of brain age based on multimodal fuzzy feature fusion

Abstract

Brain age gap can be estimated from brain images, serving as a valuable biomarker for aging-associated diseases, using deep neural networks. Traditional brain age prediction methods tend to rely on unimodal data. Multimodal data can provide more comprehensive information and improve prediction accuracy. However, existing multimodal fusion methods often fall short in fully leveraging the correlations and complementarities between different modalities. This paper introduces a novel multimodal fuzzy feature fusion collaborative prediction algorithm for brain age estimation (MFCA). The proposed approach integrates multiple imaging modalities using a fuzzy fusion module and a multimodal collaborative convolutional module to effectively leverage inter-modal correlations and complementary information. Specifically, a convolutional neural network is used to extract feature from multimodal brain images, which are then combined into a global feature tensor via radial joins. The fuzzy fusion module employs fuzzy theory to fuse the correlation features of different modalities, while the multimodal collaborative convolutional module enhances complementary information through modality-specific convolutional layers. Age prediction is then performed by an age prediction module containing three linear regression modules. Additionally, an optimized sorting contrast loss is incorporated to improve the accuracy of age prediction. The proposed method was evaluated on the SRPBS multi-disorder MRI dataset, and the experimental results demonstrate that MFCA achieves a mean absolute error of 5.661 and a Pearson correlation coefficient of 0.947, outperforming several state-of-the-art methods.