BME 499.060: AI in BME
The goal of this course is to introduce and apply AI tools to problems in BME.
AI and machine learning algorithms have had a major impact on biomedical science in the past decade. AI algorithms can learn patterns from biomedical data sets to provide actionable insights on disease diagnosis or treatment. This course will focus on practical applications of AI in BME with hands-on tutorials.
This course will provide an overview of a wide range of AI and machine-learning tools (clustering, regression, decision trees, random forests and neural networks), biomedical data sets (imaging, omics and data-mining) and diseases (cancer, infectious-, cardiovascular- and neurological-).
Software used - MATLAB. No programming experience necessary beyond Eng 101
Who is this for?
This course is intended for BME undergraduates and graduate students interested in learning AI
EECS and other students interested in biomedical applications of AI can also register if they satisfy the Prerequisites
This course is for 2 credit hours and will be on M/W from 3:30-4:30 in Dow 1010
Students need to bring their own laptops with MATLAB installed. MATLAB is available for free from CAEN.
Biology 172 or 174, Intro to Biology
Math 116, Calculus
Engineering 101, Intro to Computing
Statistics & Linear algebra (preferable, but not required)
Introduction to AI & Machine-learning (ML)
Matlab programming boot camp
Exploratory data analysis
Plotting and data visualization
Handling big-data sets
Classifying cancer sub-types using clustering
Predicting cardiovascular risk using regression
Predicting diabetic retinopathy using decision trees
Identifying synergistic drug combinations using RF
Neural Networks (NN)
Diagnosing breast cancer from biopsy images using NN
Guest lecture - AI and Omics, Kelly Arnold
Guest lecture - AI in Imaging, Jeff Fessler
Guest lecture - AI in Neural Engineering, Cindy Chestek
Presentation by student teams (2 lectures)
Assignments (50%). There will be five assignments. The assignments will involve both programming modules and biomedical modules involving discussion of salient biomedical AI studies.
Design project (50%). The final project will be done as a team of students. Each team is required to give a short presentation on their project along with a written report. Both the presentation and the report should provide a background of the biomedical problem, a description of the AI methods used, effective visualization of the results, and a discussion on possible limitations and future directions of the project.