Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET. Analysis of these images requires a comprehensive environment for data access, visualization, processing, and algorithm development. The main challenge is to extract clinically meaningful information based on advanced techniques such as Artificial Intelligence (AI). To achieve this, one needs to clean, segment, register, and label a large collection of images. For the AI analysis, there are many more challenges such as iteratively adjusting AI models or learning parameters. MATLAB provides tools such as Medical Imaging Toolbox and Deep Learning Toolbox and algorithms for end-to-end medical image analysis and AI workflow.
When: March 16, 2023, 3-4PM EDT
Register for the seminar using this link!
In this presentation, you will learn how to:
- Easily import and visualize 2D images and 3D volumes interactively
- Segment, register, and label medical image and volume data
- Import and edit pre-trained networks for processing
- Design, train, and test AI and deep learning models
About the Presenter
Dr. Elvira Osuna-Highley
Principal Education Application Engineer
Elvira Osuna-Highley, Ph.D. is part of a global team supporting academic research and teaching at MathWorks. Before joining MathWorks, she was on the faculty of the Computational Biology Department at Carnegie Mellon University. She holds a doctorate in Biomedical Engineering from Carnegie Mellon University, where her research involved applying machine learning techniques to fluorescence microscope images.