# Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound Source: [Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound]() Collection index: [llms.txt]() Data updated: 2026-01-02T10:39:22.062Z Authors: Zhiyuan Zhu, Jian Wang, Yong Jiang, Tong Han, Yuhao Huang, Ang Zhang, Kaiwen Yang, Mingyuan Luo, Zhe Liu, Yaofei Duan, Dong Ni, Tianhong Tang, Xin Yang Published: 2025-06-29T06:20:15.000Z ## Abstract Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-View-Category Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel center-memory contrastive loss, which enhances the network's global modeling capability by comparing with representative cluster centers and diverse negative samples at the Corpus level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at the View level. Finally, a parameter-free mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at the Category level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Carotid Plaque Grading on Private Carotid US Images 1.0 (test)](): Carotid plaque grading; Private Carotid US Images 1.0 (test); Accuracy: 93.25. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()