AI in Medical Imaging. 3 credits, 3 contact hours

Catalog description

This course provides an in-depth exploration of the latest artificial intelligence (AI) technologies and their applications in medical imaging. Students will learn how the latest AI methods, especially deep learning and large-scale foundation models, are transforming diagnostic imaging, image reconstruction, segmentation, registration, and clinical decision support. The course bridges theoretical foundations and practical implementation, with a focus on "vibe coding" as a paradigm for human–AI collaborative engineering. Students will gain experience developing and evaluating AI algorithms using real-world datasets from modalities such as MRI, CT, X-ray, and ultrasound. Topics include foundational deep learning architectures such as Transformers, Large Language Models (LLMs), Vision Transformers (ViTs), and Vision-Language Models (VLMs), along with key training paradigms including contrastive learning and masked image modeling. The course also introduces Agentic AI and their applications in medical imaging tasks such as image reconstruction, segmentation, and registration. Generative modeling including diffusion models will be also covered.