Transfer learning with class activation maps in compositions driving plaque classification in carotid ultrasound.

ABSTRACT:

Introduction

Carotid B-mode ultrasound (U/S) imaging provides more than the degree of stenosis in stroke risk assessment. Plaque morphology and texture have been extensively investigated in U/S images, revealing plaque components, such as juxtaluminal black areas close to lumen (JBAs), whose size is linearly related to the risk of stroke. Convolutional neural networks (CNNs) have joined the battle for the identification of high-risk plaques, although the ways they perceive asymptomatic (ASY) and symptomatic (SY) plaque features need further investigation. In this study, the objective was to assess whether class activations maps (CAMs) can reveal which U/S grayscale-(GS)-based plaque compositions (lipid cores, fibrous content, collagen, and/or calcified areas) influence the m

SUBMITTER: Liapi GD 

PROVIDER: S-EPMC12283640 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature