Machine Learning and Imaging (GE, IM)

Description from DukeHub

Deep learning is rapidly changing how we interpret image data. A large amount of research is now examining how we can use new machine learning tools to automatically interpret microscope, ultrasound and x-ray images, and MRI and CT scans, for example, to aid with diagnostic tasks. In this class, we will review how these machine learning tools work, with a particular focus on how they might be used in a diagnostic setting. This class will also investigate the specific question of how deep learning algorithms can be used to design imaging system hardware to improve performance, which will be the primary focus of the course final project. Prerequisite: BME 303L or graduate standing.

Skills

• ai and machine learningdescription: "deep learning" and 3 more
• lab workhas a lab component
• clinicaldescription: "diagnostic" and 1 more

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "diagnostic"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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