MACHINE LEARNING IN ULTRASOUND IMAGING
Concepts and machine learning techniques for ultrasound beamforming for image formation and reconstruction to image analysis and interpretation will be presented. Key machine learning and deep learning concepts applied to beamforming, compressed sampling, speckle reduction, segmentation, photoacoustics, and elasticity imaging will be evaluated utilizing current peer-reviewed publications.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Critically read and evaluate peer-reviewed journal papers describing machine learning applications in ultrasound imaging. Audience: Graduate 2. Apply, implement and expand upon ideas from these publications to applications in ultrasound imaging. Audience: Graduate 3. Present the results of their critical evaluation and implementation to the class. Audience: Graduate 4. Write a research paper based on their findings suitable for publication. Audience: Graduate Learning Outcomes: 1. Critically read and evaluate peer-reviewed journal papers describing machine learning applications in ultrasound imaging. Audience: Graduate 2. Apply, implement and expand upon ideas from these publications to applications in ultrasound imaging. Audience: Graduate 3. Present the results of their critical evaluation and implementation to the class. Audience: Graduate 4. Write a research paper based on their findings suitable for publication. Audience: Graduate
Sources
Catalogue variants
- MACHINE LEARNING IN ULTRASOUND IMAGING · 3 credits.