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Medical Image Generation and Analysis using Bayesian Generative Models

Speaker Name: 
Razvan Marinescu
Speaker Title: 
Postdoctoral Associate
Speaker Organization: 
Start Time: 
Wednesday, March 24, 2021 - 11:00am
End Time: 
Wednesday, March 24, 2021 - 12:15pm
Via Zoom Link
Alvaro Cardenas


Machine Learning algorithms have achieved impressive milestones on image generation and prediction tasks, yet these achievements have not often translated into advances for medical applications. This is because medical problems are fundamentally different than those in computer vision: (i) while medical diagnoses are often binary (healthy/disease), the disease itself is a “continuous process” from which we only observe a few snapshots at various points in time and (ii) the image data (e.g., MRI, CT) is under-sampled and corrupted by patient motion in the scanner. In this talk, I will present two generative models that tackle these issues. First, I will present (i) DIVE, a Bayesian spatio-temporal model that estimates the *continuous progression* of Alzheimer’s disease. Secondly, I will present (ii) BRGM, a Bayesian deep learning method that leverages StyleGAN2 to estimate priors over clean images, and then applies Bayes’ rule to estimate the posterior distribution over clean images given an input corrupted image. Taken together, these contributions enable Machine Learning models to correctly model medical diseases using suitable assumptions, and can make medical image acquisition significantly better, faster and cheaper.  


Razvan Marinescu is a Postdoctoral Associate at the MIT Computer Science and Artificial Intelligence Laboratory. His research interests are in Machine Learning for Healthcare, with a particular emphasis on understanding brain diseases and intelligence. Razvan received a PhD from the Center of Medical Image Computing at University College London in 2019, and holds an MRes and Bachelor’s in Computer Science from Imperial College London. Razvan’s DIVE model received the joint runner-up Francois Erbsmann Prize at the IPMI 2017 conference, and he also organised the TADPOLE Challenge for evaluating models at estimating the future progression of Alzheimer’s disease. He was the President of the MIT Postdoctoral Association during 2019-2020. 


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