CAFNet:Category-Aware Filtering Based on Mutual Information for Heterogeneous Data in Neurological Diseases

Abstract

In the diagnosis of neurodegenerative diseases, effectively integrating heterogeneous data from medical imaging and clinical diagnostic texts remains a significant challenge. In this study, we propose CAFNet, a novel collaborative learning frame-work designed to bridge the gap between structural MRI and clinical narratives through anatomically guided and symptom-aware fusion. The framework leverages a large language model to encode rich semantic information from diagnostic text, while a anatomical MaxPooling Convolution module extracts region-specific features from brain MRI volumes using directional pooling along canonical axes. These heterogeneous features are further aligned via a symptoms attention convolution and refined through a Category-Aware Filtering module based on mutual information, which adaptively enhances class-relevant repre-sentations. Experimental evaluations on three public datasets demonstrate that CAFNet significantly improves diagnostic accuracy and generalization, outperforming existing state-of-the-art multimodal classification models. The results confirm the effectiveness of integrating anatomical priors and clinical semantics for interpretable and robust brain disease classification.

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