Biomedical Image Analysis

Hands-on introduction to biomedical image analysis covering medical imaging formats, segmentation, registration, quantification, and classification.

offering recorded3 credits
Recorded instructors · Spring 2027 Juan Caicedo

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.91 GPA, 100.0% A/AB (n=17 letter grades); Spring 2025: 3.52 GPA, 65.2% A/AB (n=23 letter grades); Spring 2026: 3.92 GPA, 95.8% A/AB (n=24 letter grades).

What you’ll be able to do

  • Implement the key principles of ideas from probability, statistics and computer vision algorithms used in medical image analysis

  • Recognize which image analysis problems will benefit from which modeling approach

  • Apply algorithms about image analysis tasks and implement algorithms and pipelines using a programming language

  • Implement the building blocks taught in this course to independently learn and apply new, but related imaging analysis algorithms

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 320, 340, 341, 345, or 375) and (STAT 511, 541,POP HLTH/​B M I 551,MATH 331,MATH/​STAT 431, 309,STAT 240, 301, 311, 324, 371, or STAT/​F&W ECOL 571, or HORT 571 prior to Spring 2025) or graduate/professional standing

BMI/COMPSCI 567

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2024: 3.91 GPA, 100.0% A/AB (n=17 letter grades); Spring 2025: 3.52 GPA, 65.2% A/AB (n=23 letter grades); Spring 2026: 3.92 GPA, 95.8% A/AB (n=24 letter grades).

Historical instructors & teaching patterns

JUAN CAICEDO is recorded teaching in Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300
LEC 001Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Student experience

the class

Recent recorded grades — Spring 2024: 3.91 GPA, 100.0% A/AB (n=17 letter grades); Spring 2025: 3.52 GPA, 65.2% A/AB (n=23 letter grades); Spring 2026: 3.92 GPA, 95.8% A/AB (n=24 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Medical imaging formats, segmentation, registration, image quantification, and classification
  • Probability, statistics, and computer vision algorithms for medical image analysis

Skills

  • Implementing principles of probability, statistics, and computer vision algorithms
  • Selecting appropriate modeling approaches for image analysis problems
  • Applying and implementing image analysis algorithms and pipelines in code
  • Independently learning and applying new imaging analysis algorithms

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

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
BMI/COMPSCI 567 · Spring 2027

Biomedical Image Analysis

Recorded 2026-10-14
BMI/COMPSCI 567 · Spring 2027

Biomedical Image Analysis

Recorded 2026-10-14
Raw records
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    "credits_min": 3,
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records
Qwen/Qwen3.8-27B
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": "b9704071157f6961ca120af6f4e1200c6b838d80ac149300a405a01f8caa76e0",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}