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BVISS: Learning to synthesize signals and images
Friday 17, November, 2017 @ 4:00 pm - 5:00 pm
Dr Sotirios Tsaftaris – School of Engineering, Edinburgh University
Abstract: An increasing population and climate change put pressure on several societally important domains. Health costs are increasing and at the same time feeding the world becomes a challenge. Imaging (and sensing) is central to furthering our understanding of biology not
only in its diagnostic capacity but also in phenotyping variation. This opens the need for several analysis tasks of detection, segmentation, classification etc on the basis of static or dynamic imaging data. Evaluating and designing algorithms that address these tasks relies heavily on real annotated data, of sufficient quality and quantity. Synthetically generating data with ground truth can be a useful alternative. In this seminar I will motivate this using two application domains: medical imaging and plant phenotyping. I will present solutions that learn in data-driven fashion data distributions and mappings that generate or synthesize data using dictionaries or deep neural networks. Our approaches use structured learning and multiple modalities to learn representations of desirable invariance (and covariance). Problems of crossmodal synthesis in MRI and CT are presented as well as the ability to conditionally generate images of plants with specific topological arrangement.
Bio: Dr. Sotirios A. Tsaftaris, 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 a Chancellor’s Fellow (Senior Lecturer grade) in the School of Engineering at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute.
From 2006 to 2011, he was a research assistant professor with the Departments of EECS and Radiology, Northwestern University, USA. From 2011-2015, he was with IMT Institute for Advanced Studies, Lucca serving as Director of the Pattern Recognition and Image Analysis Unit.
He is an Associate Editor for the IEEE Journal of Biomedical and Health Informatics and for Digital Signal Processing – Journal (Elsevier). He has organized specialized workshops at ECCV (2014), BMVC (2015), ICCV (2017) and MICCAI (2016,2017), and served as Area Chair for IEEE ICCV (2017) and VCIP (2015). He has also served as guest editor (Machine Vision and Applications; IEEE Transactions on Medical Imaging; and Digital Signal Processing – Software X).
He has received twice the Magna Cum Laude Award by the International Society for Magnetic Resonance in Medicine (ISMRM) in 2012 and 2014, and was a finalist for the Early Career Award, from the Society for Cardiovascular Magnetic Resonance (SCMR) in 2011.
He has authored more than 100 journal and conference papers particularly in interdisciplinary ﬁelds and his work is (or has been) supported by the National Institutes of Health (USA), EPSRC & BBSRC (UK), the European Union, the Italian Government, and several non-profits and industrial partners.
His research interests are in machine learning, image analysis (medical image computing), image processing, and distributed computing.
Dr. Tsaftaris is a Murphy, Onassis, and Marie Curie Fellow. He is also member of IEEE, ISMRM, SCMR, and IAPR.