HAI Weekly Seminar with Akshay Chaudhari
Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition
Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition
How did we get to today’s technology which now supports a trillion dollar AI industry? What were the key scientific breakthroughs? What were the surprises and dead-ends along the way...

How did we get to today’s technology which now supports a trillion dollar AI industry? What were the key scientific breakthroughs? What were the surprises and dead-ends along the way...
The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.

The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.
Recent applications of artificial intelligence (AI) in radiology have focused on image interpretation tasks such as image classification, segmentation, or detection. However, a fundamental challenge in radiology is to acquire these medical images in a safe and efficient manner. New AI techniques have been proposed to solve the inverse problem of image reconstruction wherein only a limited set of measurements are used to reconstruct medical images with high diagnostic quality. Specifically, in this talk, I will describe how physics-guided AI is currently being used to improve the speed of magnetic resonance imaging (MRI). I will further describe how we may eschew requiring large extents of paired datasets required for supervised model training by using novel unsupervised and semi-supervised approaches for accelerated MRI. Beyond data efficiency, these approaches can help mitigate the challenge of distribution shifts for trained models. I will conclude by describing a 1.5TB dataset that we have made publicly available to help evaluate MRI reconstructions with clinically-relevant metrics.
