UW Radiology

Deep Learning Pathway

The UW Radiology Deep Learning Pathway is an immersive and rigorous experience that trains residents to apply cutting-edge deep learning techniques to medical imaging research. This unique resident training path is the first of its kind to bridge the the gap between medical imaging and AI education, and was co-founded by UW resident Mazen Zawaideh, alongside mentors Drs. David Haynor and Nathan Cross. 

Deep learning is emerging as a highly promising class of machine learning techniques that allows physicians and engineers to tackle clinical problems that were computationally infeasible just a few years ago. These deep learning methods are setting a new standard in imaging research, and radiologists who are equipped to utilize them will be highly sought after.

In this pathway, residents learn the foundations of deep learning within the domain of medical imaging. The course consists of structured online video lectures, programming assignments, and small-group collaborations. Upon completion, residents will have:

-Understood how to frame clinical problems as machine learning tasks

-Learned to build and train neural networks

-Learned to lead successful machine learning projects as a clinical domain expert

-Applied their knowledge by forming a deep learning team and complete a deep learning project from beginning to end

Upon completion, the resident is awarded a certificate by the UW Department of Radiology to indicate mastery of both deep learning theoretical foundations and practical application in the clinical realm.

To learn more, visit www.imagedeep.io