Module 4: Classification and detection

Theme

Classification and detection

Essential Question

How do imaging models identify findings?

Module Components

• Book prose: conceptual framing, domain scenario, methods, and failure modes

• Assignment: evidence-backed production of a specific artifact

• Slides: presentation sequence for seminar or lecture delivery

• Narration: spoken version of the slide flow

• Rubric: criteria for evaluating the module artifact

• Notebook: executable lab aligned with the module theme using synthetic 8x8 grayscale image arrays with a small bright lesion pattern and non-lesion variation

Module Artifact

imaging model evidence packet with preprocessing notes, validation limits, and clinical handoff risks focused on classification and detection: Train or simulate an image classification baseline.

Professional Setting

Students work as if advising a radiology service deciding whether an imaging AI should advance from retrospective review to supervised pilot use. Their work must be intelligible to radiologist, imaging technician, compliance officer, and patient safety lead.

Use This Module in Order

1. Read the learning chapter.

2. Review the slide deck with the matching narration.

3. In Populi, open the private student-repository link for this course and enter modules/module-4.

4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.