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Abstract #3595

Exploiting Coarse-Scale Image Features for Transfer Learning in Accelerated Magnetic Resonance Imaging

Ukash Nakarmi1, Joseph Y. Cheng1, Edgar P. Rios1, Morteza Mardani1, John M. Pauly2, and Shreyas S Vasanawala1
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States

This work investigates coarse-scale image features for transfer learning in accelerated magnetic resonance imaging. The model uses multi-scale unrolled CNN architecture that captures image features at coarse and fine scale to efficiently reduce the training sample size for deep learning model training.

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