MMFN: Multi-Feature Multi-Modal Fusion Network for Diagnosis of Superficial Lymph Node Disease

Abstract

The difficulty in identifying lymph node malignancies, including lymphoma and metastatic tumors, pose a diagnostic challenge at their primary sites. Given the heterogeneity of lymph node structures across different regions and the difficulty in distinguishing them from surrounding tissues, accurate diagnosis is often impeded. This research introduces multi-feature multi-modal fusion network (MMFN) for the differential diagnosis of benign and malignant lymph node diseases. The network integrates a convolusional neural network(CNN)-branch and a vision transformer(ViT)-branch to extract multi-scale features from ultrasound (US) and color doppler flow imaging (CDFI) images. By incorporating the convolutional block attention (CBA) module and cross modal attention (CMA) module, the network facilitates feature interaction and fusion across scales, leveraging blood flow information to enhance edge area detection. Furthermore, the feature fusion module (FFM) enables the interweaving of features from different dimensions, thereby enriching representational learning. Through experiments on private dataset, our approach demonstrates superior performance over existing methods.