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Abstract #0682

Visualizing and utilizing latent features of MR vessel wall images using weakly supervised deep learning analysis workflow

Li Chen1, Wenjin Liu 1, Gador Canton 1, Niranjan Balu 1, Thomas Hatsukami 1, John C. Waterton 2, Jenq-Neng Hwang 1, and Chun Yuan 1
1University of Washington, Seattle, WA, United States, 2Centre for Imaging Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom

Atherosclerotic plaque information can be extracted from MR vessel wall images through transforming the images into a high dimensional feature space. However, a huge amount of human supervision has traditionally been required to achieve a meaningful feature space representation. We demonstrated that by using a weakly supervised deep learning workflow including transfer learning, active learning, and metric learning, a meaningful feature space for vessel wall analysis can be generated, which can help us to visualize the high dimensional representations of normal and diseased vessel walls images, and lead to a plaque classification area under the curve of 0.93.

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