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Abstract #0983

Automatic AHA Classification of Carotid Atherosclerotic Lesions in Multicontrast MR Images using Deep Learning

Jifan Li1, Shuo Chen1, Xihai Zhao1, Yuan Chun2, and Rui Li1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China, Beijing, China, 2Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States, Seattle, WA, United States

In this study, we aimed to develop a convolutional neural network (CNN) to classify carotid atherosclerotic lesions in high-resolution multicontrast MR images automatically using the modified American Heart Association (AHA) classification scheme as criteria. The network was trained on a large number of plaque images combined with lesion type labeled by experienced radiologists. Transfer learning was utilized to take the advantage of state-of-the-art CNN pre-trained on ImageNet dataset. The accuracy of lesion type classification achieved 85.1% with preprocessing and fine-tuning of the network.

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