"Generalizable and democratic AI:
from classical techniques to modern neural networks"

Juan Eugenio Iglesias
Associate Professor of Radiology
MGH & Harvard Medical School
Research affiliate, Massachusetts Institute of Technology
Honorary Research Fellow, University College London
https://lemon.martinos.org/

Abstract:

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research – particularly for underrepresented populations that research-oriented datasets like ADNI or UK BioBank often neglect. However, the potential of these scans remains untapped since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artifacts, and subject populations). In this talk, I will present techniques developed by our group over the last few years that enable robust analysis of heterogeneous clinical datasets “in the wild”, including segmentation, registration, super-resolution, and synthesis. The talk will cover both: (i) classical Bayesian techniques, based on high-resolution atlases derived from ex vivo MRI and histology, and (ii) modern neural networks, relying on domain randomization methods for enhanced generalizability. I will present results on thousands of brain scans from our hospital with highly heterogeneous orientation, resolution, and contrast, as well as results on low-field scans acquired with a portable scanner.

"NEUROTEC Lecture Series", features international experts who will provide inspirational and motivational value to students and young researchers on neuroscience and neurotechnology.