Abstract #2659

Exploring feature space of MR vessel images with limited data annotations through metric learning and episodic training

Kaiyue Tao¹, Li Chen², Niranjan Balu³, Gador Canton³, Wenjin Liu³, Thomas S. Hatsukami⁴, and Chun Yuan³

¹University of Science and Technology of China, Hefei, China, ²Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States, ³Department of Radiology, University of Washington, Seattle, WA, United States, ⁴Department of Surgery, University of Washington, Seattle, WA, United States

Popliteal vessel wall features hidden in the Osteoarthritis Initiative (OAI) dataset warrant further investigation. However, if the number of annotations is insufficient, deep learning-based feature map analysis from MRI images may overfit and fail to generate a meaningful feature space. We designed a metric learning network combined with an episodic training method to overcome the problem of limited annotations, and demonstrated its ability to learn a meaningful feature embedding. Based on our feature map, we proposed an iterative workflow and identified vessel wall images with high probability of diseases from 1974 cases.

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