Plaque Classification

Plaque classification aims to automatically identify and characterize atherosclerotic plaques in vascular imaging, crucial for assessing cardiovascular risk and guiding treatment. Current research focuses on improving the accuracy and efficiency of plaque classification using deep learning models, such as convolutional neural networks and transformers, often incorporating auxiliary tasks like segmentation to enhance performance. These advancements leverage advanced image processing techniques and are applied to various imaging modalities, including ultrasound and optical coherence tomography, to improve the objectivity and reproducibility of plaque analysis. Ultimately, accurate automated plaque classification holds significant promise for improving patient care and advancing cardiovascular research.