Webinar

How Computer Vision is Powering Medical Advancements

On Demand
40 minutes

How do medical researchers use AI to predict which patients might respond well to particular treatments? David Van Valen, MD PhD, at Caltech is using deep learning to do just that. His team trains neural networks to automate cell identification in live-cell imaging to both understand how the immune system interacts with tumors and ultimately improve patient outcomes.

This webinar will be relevant to anyone using machine learning with time-lapse data or video and applying deep neural networks to computer vision.

Speaker

David Van Valen, MD, PhD

The Van Valen Lab, Caltech

David Van Valen studied mathematics and physics at MIT, applied physics at Caltech, and medicine at UCLA. Starting September 2018, he will be starting his own research group in the Division of Biology and Bioengineering at Caltech. When not busy with his studies, David enjoys spending time with family and friends, reading various works of literature, and the martial arts.

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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.
Speaker

David Van Valen, MD, PhD

The Van Valen Lab, Caltech

David Van Valen studied mathematics and physics at MIT, applied physics at Caltech, and medicine at UCLA. Starting September 2018, he will be starting his own research group in the Division of Biology and Bioengineering at Caltech. When not busy with his studies, David enjoys spending time with family and friends, reading various works of literature, and the martial arts.

Name Last

Job Title

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.

Name Last

Job Title

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent neque elit, blandit nec aliquam tincidunt, tristique id lorem. Aliquam dapibus bibendum mauris. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus quis eleifend purus. Nunc eget nibh ante. Cras nec nisi vel dolor eleifend aliquam.