BMI 6331 Medical Imaging and Signal Pattern Recognition

3 semester credit hours
Lecture contact hours: 2; Lab contact hours: 3
Web-based and classroom instruction
Prerequisite: BMI 5007
Lab Fee: $30

Course Description
Biomedical data in the form of images, videos or other unstructured signals are continuously collected by clinicians, such as radiologists, dermatologists or ophthalmologists, life science researchers and increasingly by ourselves with our personal devices. Tools able to distill quantitative actionable information from these data are essential to generate phenotypes, aid diagnosis, screening, treatment and automate repetitive tasks. In the era of personalized medicine and big data, they have become an urgent medical need. In this course, you will be introduced to the essential pattern recognitions techniques to build and evaluate such tools. We will be covering the basics of image/signal processing, computer vision and applied machine learning with hands on examples relevant to biomedical applications.

Learning Objectives
Upon successfully completing this course, students will:

• Design an image pre-processing pipeline suited to biomedical data
• Compose an image segmentation approach using rule-based algorithms
• Assess the correct strategy for medical image registration/mosaicing problem
• Design a machine learning model for medical image segmentation
• Design a machine learning predictive model from biomedical images and signals
• Structure complex heterogeneous data for integrative analysis
• Evaluate and interpret analysis results