Discover the Power of Deep Learning for Medical Imaging
Modern healthcare offers a vast variety of complementary medical imaging data: from x-rays, CT scans, MRI, to name a few. But with an increasing amount of data, comes an increase in workload for specialists who must interpret, compare, and analyse this information as quickly as possible, without compromising on accuracy. To support doctors and to facilitate the workflow, computer assistance has become common practice for medical procedures. At the same time, there has been tremendous progress in the field of artificial intelligence (AI) and, in particular, deep learning. These techniques have shown huge potential for addressing the challenges faced in medical imaging processing and, as a result, innovative methods and applications based on this technology have already been developed and adopted worldwide.
This webinar introduces you to deep learning in the field of medical imaging and will explore how to implement the best practices from computer science in the medical field.
Highlights include:
- Introduction to deep learning and what distinguishes it from traditional AI.
- The key challenges, requirements and opportunities to leverage this technology.
- How to get started and an overview of solutions to address your needs.
ONDEMAND WEBINAR REGISTRATION
Tuesday, July 31, 2018
11:00am – 12:00pm CET
1 Hour
Webinar access will be emailed to you.
Presented By
Nicola Rieke
Senior Solutions Architect for Deep Learning in Healthcare, NVIDIA
Nicola Rieke is a solution architect at NVIDIA for deep learning in healthcare with several years of experience in the intersection of mathematics, medicine and computer science. With a broad expertise in the field of medical image processing, computer-aided medical procedures, and applied machine learning, her primary responsibility is to support the medical imaging community in advancing deep learning solutions. She published various peer reviewed papers, in particular on real-time machine learning approaches for computer assistance in surgical interventions, and was honored with the prestigious MICCAI Young Scientist Award during her doctoral study at the Technical University of Munich.
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