Learning Robust Representations for Carotid Plaque Classification via Transition Matrix and Structural Similarity Regularization

Yongrui Lv, Ran Zhou, Cong Wu, Ji Wang, Haitao Gan

Abstract: The classification of carotid plaques is crucial for assessing the risk of cardiovascular diseases. Recently, deep learning has emerged as a promising solution for automatic carotid plaque classification. However, its data-driven nature makes models prone to bias from noisy labels, often caused by inconsistent and unreliable labeling, which can significantly degrade model performance. To address this issue, this paper proposes a robust carotid plaque classification method (TMSS) designed to effectively learn from datasets with noisy labels using transition matrix and structural similarity regularization. The general framework of TMSS comprises an unsupervised constructive learning branch and a supervised classification branch, regularized by structural similarity to maximize the agreement of sample relationships between the feature space and the prediction space. The transition matrix is learned to further correct the noisy labels in the classifier branch. Evaluated on 1,270 carotid ultrasound images, experimental results demonstrate that TMSS achieves significant improvements in classification performance over state-of-the-art approaches. The proposed method enhances diagnostic accuracy by mitigating label noise and improving model robustness in carotid plaque classification, thereby facilitating early disease detection and enabling personalized treatment.

External IDs:dblp:conf/ijcnn/LvZWWG25