Date: 19 - 20 September 2024

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Medical image analysis holds immense value in modern healthcare, offering potential to revolutionize diagnostics, treatment planning, and patient care. By leveraging advanced technologies such as machine learning and computer vision, medical image analysis allows for precise and efficient interpretation of complex visual data from various imaging modalities like X-rays, Magnetic resonance imaging (MRI), and Computed Tomography scan (CT) scans.  Dive into the dynamic realm of medical image analysis with our introductory 2-day course, tailored to match the rapid pace of both the field and its foundational technology—computing. Structured to be practical and contemporary, the course takes a ‘hands-on’ approach, guiding participants from introductory imaging concepts through the development of compact machine learning pipelines for inference. By the workshop’s conclusion, attendees will not only have acquired a nuanced understanding of medical imaging intricacies but will also possess the requisite tools for autonomous image analysis. Furthermore, participants will be introduced to key publicly-available resources, ensuring they depart with a valuable skill set poised to contribute to advancements in this crucial field.

City: Toronto

Region: Ontario

Country: Canada


Participants should be comfortable writing and reading basic Python. Scripting will be primarily in Google Colab notebook; it is recommended that participants have a Google account and a computer that can run the latest Google Chrome.  This workshop requires participants to complete pre-workshop tasks and readings. 

Learning objectives:

Participants will gain practical experience and skills to be able to: Understand key differences in medical imaging modalities, Learn basic image processing techniques, Process raw clinical images into analysis ready formats, Locate and download publicly available imaging data sets, Extract imaging features and train machine learning models for clinical prediction, Familiarize yourself with auto-segmentation tools and build deep learning models for medical image segmentation.

Capacity: 30

Event types:

  • Workshops and courses

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