Abstract：Deep neural networks can be trained to predict age from brain image and the predicted brain age serves as a biomarker for identifying diseases associated with aging. Traditional brain age prediction methods tend to rely on unimodal image data, whereas multimodal data can provide more comprehensive information and improve prediction accuracy. However, existing multimodal fusion methods often fail to fully exploit the correlations and complementarities between different modalities. To overcome these challenges, a collaborative brain age prediction algorithm based on multimodal fuzzy feature fusion(CMFF) is proposed. Fuzzy fusion module and multimodal collaborative convolution module are designed to effectively utilize the correlation and complementary information between the multimodal information. Firstly, feature tensors are extracted from multimodal brain images by a convolutional neural network, and are integrated into a global feature tensor via radial joins. Then, the fuzzy fusion module is employed to learn the fuzzified features, and these features are applied to the multimodal collaborative convolutional module to enhance the complementary information of these features through modality-specific convolutional layers. Finally, the age prediction regression task is performed based on the gender information and the fuzzy collaborative processed features to obtain an accurate predicted age. Experimental results on SRPBS multi-disorder MRI dataset demonstrate the superior performance of CMFF.
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