Statistical Methods for Medical Image Analysis
This course introduces key statistical methods for analyzing medical images, using up-to-date tools on public and provided datasets.
Summary
Recent recorded grades — Spring 2020: 3.92 GPA, 100.0% A/AB (n=6 letter grades); Spring 2022: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Spring 2025: 3.25 GPA, 50.0% A/AB (n=6 letter grades).
What you’ll be able to do
Implement the key concepts of biomedical image processing and analysis
Describe the key concepts of statistical inference procedures for single and multiple images
Apply scalable computation in breaking bigger imaging problems into smaller computable problems
Describe functional data analysis (FA), geometric data analysis (GDA) and topological data analysis (TDA) methods in analyzing biomedical images
Source: UW Guide 1· UW Guide 2
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
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
Recent recorded grades — Spring 2020: 3.92 GPA, 100.0% A/AB (n=6 letter grades); Spring 2022: 4.00 GPA, 100.0% A/AB (n=6 letter grades); Spring 2025: 3.25 GPA, 50.0% A/AB (n=6 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Statistical methods for medical image analysis
- Functional, geometric, and topological data analysis
Skills
- Implementing key concepts of biomedical image processing and analysis
- Describing key concepts of statistical inference procedures for single and multiple images
- Applying scalable computation to break bigger imaging problems into smaller computable problems
- Describing functional, geometric, and topological data analysis methods for biomedical images
Grades
Latest available · Spring 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2025
More grade details Grade mix, volume & source data
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
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": "2dcd4f5eb3fdf41fac82fff3acc9730f64bea86b80634961c675a3ad4c45cd04",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}