VDPF: Enhancing DVT Staging Performance Using a Global-Local Feature Fusion Network

Deep Vein Thrombosis (DVT) presents a high incidence rate and serious health risks. Therefore, accurate staging is essential for formulating effective treatment plans and enhancing prognosis. Recent studies have shown the effectiveness of Black-blood Magnetic Resonance Thrombus Imaging (BTI) in differentiating DVT stages without necessitating contrast agents. However, the accuracy of clinical DVT staging is still limited by the experience and subjective assessments of radiologists, underscoring the importance of implementing Computer-aided Diagnosis (CAD) systems for objective and precise DVT staging. Given the small size of thrombi and their high similarity in signal intensity and shape to surrounding tissues, precise staging using CAD technology poses a significant challenge. To address this, we have developed an innovative classification framework that employs a Global-Local Feature Fusion Module (GLFM) for the effective integration of global imaging and lesion-focused local imaging. Within the GLFM, a cross-attention module is designed to capture relevant global features information based on local features. Additionally, the Feature Fusion Focus Network (FFFN) module within the GLFM facilitates the integration of features across various dimensions. The synergy between these modules ensures an effective fusion of local and global features within the GLFM framework. Experimental evidence confirms the superior performance of our proposed GLFM in feature fusion, demonstrating a significant advantage over existing methods in the task of DVT staging. The code is available at https://github.com/xiextong/VDPF .