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BME 495-0-01: Deep Learning for Biomedical Imaging


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Prerequisites

Good knowledge of programming (Python including familiarity with PyTorch); basic knowledge of linear algebra and calculus; basic knowledge of image processing and machine learning is preferred

Description

Imaging science is experiencing tremendous growth. The New York Times recently ranked biomedical engineering jobs as the number one fastest growing career field in the nation and listed biomedical imaging as a primary reason for the growth. Medical imaging (as a core component of biomedical imaging) and its analysis are fundamental to understanding, visualizing, and quantifying medical images in clinical applications. With the help of automated and quantitative image analysis techniques, disease diagnosis will be easier/faster and more accurate, leading to significant development in medicine in general. The department of BME, in conjunction with Machine and Hybrid Intelligence Lab of Radiology, offers this timely course to introduce medical image analysis with deep learning techniques. Students will learn how to apply deep learning algorithms to analyze and interpret medical images such as X-rays, CT scans, and MRI scans. The course will cover basic image processing techniques, the fundamentals of deep learning, and their applications in medical image analysis. 

Who Takes It?

MS, PhD, and BS who have prerequisites knowledge.
This course is designed for graduate students, but undergraduate students are welcome to take the course too.

OBJECTIVES

  • Understand the basic concepts of medical image analysis and deep learning.  
  • Develop practical skills for using deep learning techniques for medical image analysis.  
  • Understand how deep learning algorithms can be applied to real-world medical image analysis problems.  
  • Be able to implement deep learning algorithms using open-source libraries such as PyTorch.  
  • Learn how to evaluate and validate the performance of deep learning algorithms in medical image analysis. 
Syllabus