Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound
Journal:
arXiv
Published Date:
Jun 29, 2025
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.
