Abstract #2934

On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging

Mamata Shrestha¹, Nian Wang^2,3, and Ukash Nakarmi¹

¹Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, United States, ²Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, ³Stark Neurosciences Research Institute, Indianapolis, IN, United States

Synopsis

Keywords: Image Reconstruction, Data Processing, Image super-resolution, model bias, deep learningDeep Learning based image super-resolution methods are biased towards training data modeling. Generalizability of DL based super-resolution frameworks can be improved by introducing variability and diversity in the training data.

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