Machine Learning in Ultrasound Imaging
Graduate course covering machine learning and deep learning techniques for ultrasound beamforming, image reconstruction, and analysis using peer-reviewed publications.
Summary
Lectures are small, with a median of 2 enrolled students in the recorded sections.
What you’ll be able to do
Critically read and evaluate peer-reviewed journal papers describing machine learning applications in ultrasound imaging.
Apply, implement and expand upon ideas from these publications to applications in ultrasound imaging.
Present the results of their critical evaluation and implementation to the class.
Write a research paper based on their findings suitable for publication.
Source: UW Guide 1· UW Guide 2· UW Guide 3
Grade history
↗No recorded grade history.
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Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
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Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
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.
difficulty & workload
No workload feedback recorded.
Topics
- Ultrasound beamforming, image formation, and reconstruction.
- Deep learning applications in compressed sampling, speckle reduction, segmentation, photoacoustics, and elasticity imaging.
Skills
- Machine learning concepts and techniques for ultrasound beamforming, image formation, and reconstruction.
- Evaluation of deep learning applications in compressed sampling, speckle reduction, segmentation, photoacoustics, and elasticity imaging.
- Critical reading and evaluation of peer-reviewed journal papers on machine learning in ultrasound imaging.
- Writing a research paper suitable for publication based on course findings.
Grades
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Catalog & offerings
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Raw records
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "8fec42006b55a40600a03480b6508d31a66cf94dea7e01ea32ea60cbbd0a8e95",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}