Key result
CEGF-Net improves overall carotid plaque classification accuracy by ~2% versus baseline networks.
Why the study?
Does CEGF-Net improve classification accuracy of carotid plaques in ultrasound images compared to a baseline network?
Does CEGF-Net improve classification accuracy of carotid plaques in ultrasound images compared to a baseline network?
The proposed CEGF-Net improves the classification accuracy of carotid plaques, particularly difficult-to-classify mixed-echoic plaques, on ultrasound imaging.
May aid mixed-echoic plaque classification on ultrasound; leaves open prospective validation before clinical use.
Carotid ultrasound image classification plays a vital role in carotid plaque diagnosis and stroke risk prediction. However, mixed-echoic plaques are difficult to classify because of low contrast between plaques and surrounding tissues and insufficient global contextual modeling. To address these issues, this paper proposes a Contrastive Enhancement and Context Guided Fusion Network (CEGF-Net) for carotid plaque classification. The proposed network contains a Contrast-Enhanced Feature Fusion (CEFF) module and a Context Guided Fusion (CGF) module. CEFF enhances the contrast between plaque foreground and surrounding tissue background, while CGF adaptively integrates local details and global contextual cues to improve the representation of ambiguous plaque regions. Experiments on 1270 carotid plaque ultrasound images collected from a collaborating hospital show that CEGF-Net improves classification accuracy by approximately 1.9% compared with the baseline network. In particular, the accuracy for mixed-echoic plaque classification increases by around 9%, demonstrating the effectiveness of the proposed approach.
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Dai et al. (2026) studied Carotid plaque (n=1,270). Contrastive Enhancement and Context Guided Fusion Network (CEGF-Net) vs. Baseline network was evaluated on Classification accuracy (1.9% improvement). The Contrastive Enhancement and Context Guided Fusion Network (CEGF-Net) improved carotid plaque classification accuracy by approximately 1.9% overall and 9% for mixed-echoic plaques compared with the baseline network.
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