bibtext: @InProceedings{ Xie_VDPF_MICCAI2024,
                   author = { Xie, Xiaotong and Ye, Yufeng and Yang, Tingting and Huang, Bin and Huang, Bingsheng and Huang, Yi },
                   title = { { VDPF: Enhancing DVT Staging Performance Using a Global-Local Feature Fusion Network } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 thrombus 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 thrombus 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.
title: VDPF: Enhancing DVT Staging Performance Using a Global-Local Feature Fusion Network
authors: Xie, Xiaotong Ye, Yufeng Yang, Tingting Huang, Bin Huang, Bingsheng Huang, Yi