Webinar: "Medical Imaging with Azure Machine Learning"

To access this webinar, please register here: https://hubs.li/Q012YnqY0

Topic: Medical Imaging with Azure Machine Learning

Speaker#1: Andreas Kopp, Solution Architect DS&AI at Microsoft

As a Microsoft Solution Architect for AI and Data Science, Andreas Kopp advises enterprise customers on the planning and implementation of AI based business solutions. He is focused on Azure Machine Learning, Cognitive Services and Applied AI solutions including medical imaging and responsible AI frameworks and tools.

Speaker#2: Harmke Alkemade, Cloud Solution Architect DS&AI at Microsoft

Harmke Alkemade supports customers in various industries with Data Science and AI usecases that leverage the Azure services, in the role of Cloud Solution Architect Data Science & AI in a global team.

Previously she worked as a Cloud Solution Architect for Data & AI in the Dutch subsidiary of Microsoft. Before joining Microsoft, Harmke finished her graduate program in Artificial Intelligence and worked on various data science and programming projects.

Speaker#3: Ivan Tarapov, Principal Group Manager - Medical Imaging AI at Microsoft

Ivan Tarapov is a Principal Group Manager at Healthcare AI group at Microsoft Healthcare and Life Sciences, Redmond where he works on novel techniques for the development of healthcare AI solutions. In the past Ivan had worked on Project InnerEye at Microsoft Research where he contributed to development of state-of-the-art AI systems for automatic segmentation of CT and MRI scans, and created a module on 3D medical imaging for Udacity's "AI for Healthcare" online nanodegree. Prior to joining Microsoft Ivan worked in global consultancy roles, contributing to mission-critical medical software projects.

Abstract:
The purpose of this session is to demonstrate how Azure Machine Learning can be used to support medical imaging and other use cases in areas like data and model management, deployment, experiment tracking and explainability. Our demo repository covers various data science approaches ranging from manual model development with PyTorch to automated machine learning for images and these approaches will be demonstrated in this session.

An overview of the content discussed in this session can be found in this blog post - https://towardsdatascience.com/medical-imaging-with-azure-machine-learning-b5acfd772dd5
The repository with the demos can be found here - https://github.com/Azure/medical-imaging

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