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The building blocks of a Big AI in healthcare

Abstract: Healthcare is under a perfect storm with AI/ML being offered as a solution to relieve at least one bottleneck: making decisions and predictions. Indeed, the detection of disease, segmentation of anatomy and other classical image analysis tasks, have seen incredible improvements due to deep learning. Yet these advances need lots of data: for every new task, new imaging scan, new hospital, new population, more training data are needed. The current paradigm of AI provides many tailored, laborious to train and develop, models. We need a new vision: a “Big AI” that can process several inputs, solve several tasks, generalize to new data, and make well-grounded predictions all relying on less supervision. I will advocate that central to paving the way for solutions that address such desiderata are better data representations learned without supervision.  The presentation from a technical viewpoint will touch on multimodal learning, disentangled representation learning, meta-learning, semi- and weak-supervision, generative models, and causality.  

Biosketch Sotirios A. Tsaftaris

Prof. Sotirios A. Tsaftaris, or Sotos, (https://vios.science; @STsaftaris), obtained his PhD and MSc degrees in Electrical Engineering and Computer Science (EECS) from Northwestern University, USA in 2006 and 2003 respectively. He obtained his Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece. 

Currently, he is the Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI, and the Chair in Machine Learning and Computer Vision at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute and an ELLIS Fellow. Previously he was an Assistant Professor with IMT Institute for Advanced Studies, Lucca, Italy and Director of the Pattern Recognition and Image Analysis Unit at IMT (2011-2015). Prior, he held a joint Research Assistant Professor appointment at Northwestern University with the Departments of Electrical Engineering and Computer Science (EECS) and Radiology Feinberg School of Medicine (2006-2011).

He is an Associate Editor for IEEE Transactions on Medical Imaging. He was tutorial chair for ECCV 2020. He was Doctoral Symposium Chair for IEEE ICIP 2018 (Athens). He served as area chair for CVPR 2021, IEEE ICME 2019, MICCAI 2020/2018, ICCV 2017, MMSP 2016, and VCIP 2015. He has also co-organized workshops for CVPR (2019), ICCV (2017), ECCV (2014), BMVC (2015, 2018), and MICCAI (2016, 2017) and delivered tutorials to ICASSP (2019) and MICCAI (2020).

He has received best paper awards (MIDL 2021, DART 2021, STACOM 2017), twice the Magna Cum Laude Award (ISMRM in 2012 and 2014), and was a finalist for the Early Career Award (Society for Cardiovascular Magnetic Resonance, SCMR, in 2011 and 2019).

He has authored more than 160 journal and conference papers particularly in interdisciplinary fields and his work is (or has been) supported by the National Institutes of Health (USA), RAEng, EPSRC & BBSRC (UK), the European Union, and several non-profits and industrial partners.

His research interests lie in machine learning, computer vision, and image analysis.

Dr. Tsaftaris is a Murphy, Onassis, and Marie Curie Fellow. He is also member of IEEE, ISMRM, SCMR, and IAPR.

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