AINS6100: AI in Medical Imaging#
Aurnova MSAI track: Healthcare AI
Credits: 3
Format: 8-week online graduate course
Applies AI to clinical imaging workflows, labels, preprocessing, classification, segmentation, validation, and regulation.
This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.
Course Outcomes#
By the end of the course, students will be able to:
explain the major concepts and tradeoffs in AI in Medical Imaging;
build or evaluate applied AI artifacts aligned with the course domain;
document assumptions, evidence, limitations, and operational risks;
connect technical work to governance, stakeholder needs, and deployment readiness.
Module Map#
Clinical imaging workflows — Where can AI help without disrupting clinical accountability?
Image data, labels, and annotation — How do labeling practices shape medical model validity?
Preprocessing and augmentation — Which transformations preserve clinical meaning?
Classification and detection — How do imaging models identify findings?
Segmentation and measurement — How do models support localization and quantification?
Validation, bias, and safety — What evidence is needed before clinical use?
Regulatory and operational integration — How does an imaging AI enter practice responsibly?
Medical imaging AI case review — What would make the system clinically credible?