Basic Information
The Course Will Provide an Overview of Recent Advances In Deep-learning Applied to Magnetic Resonance Imaging (mri). We Will Briefly Introduce The Theoretical and Practical Aspects of Mri. Then We Will Discuss How Deep-learning Has Been Applied to The Entire Mri Processing Chain Including Acquisition, Reconstruction, Restoration, Registration, Segmentation, and Diagnosis. Learning Outcomes# At The End of The Course, Students Will Know# 1. How to Formulate a Challenge in Mri Processing Pipeline By A Deep-neural-network. 2. How to Implement a Deep-neural-network to Solve a Given Challenge Such As# A. Segmentation. B. Registration. C. Reconstruction/restoration. D. Disease Prediction. E. Quantitative Mri Analysis (specifically, Diffusion-weighted Mri) 3. How to Train and Evaluate a Deep-neural-network to Solve A Challenge in Mri.
Faculty: Biomedical Engineering
|Undergraduate Studies
|Graduate Studies
Pre-required courses
(46195 - Machine Learning and 336027 - Medical Image Processing and 336207 - Medical Image Processing and 336504 - Principles of Mri) or (46195 - Machine Learning and 46200 - Image Processing and Analysis and 336504 - Principles of Mri) or (46200 - Image Processing and Analysis and 336502 - Principles of Medical Imaging and 336546 - Machine Learning in Healthcare) or (46200 - Image Processing and Analysis and 336502 - Principles of Medical Imaging and 336546 - Machine Learning in Healthcare) or (336027 - Medical Image Processing and 336207 - Medical Image Processing and 336504 - Principles of Mri and 336546 - Machine Learning in Healthcare) or (336027 - Medical Image Processing and 336207 - Medical Image Processing and 336502 - Principles of Medical Imaging and 336546 - Machine Learning in Healthcare)
Course with no extra credit
338028 - Deep-learning Applications in Mri