Special Session 2

Special Session Title: Signal/Image Processing-Machine Learning for Physiological Signals

Organisers: Arash Mohammadi (arash.mohammadi@concordia.ca), Concordia University, Montreal, QC, Canada
Hojjat Salehinejad (salehinejad.hojjat@mayo.edu), Mayo Clinic, Rochester, MN, USA

Keywords: Artificial intelligence, bioinformatics, electronic healthcare records, deep learning, machine learning, medical imaging, signal processing

Description:

Recent advances in artificial intelligence, signal processing, and high-performance computing have leveraged novel techniques in extracting insights from big data. The natural complex structure of medical data makes development of machine learning and knowledge extraction methods more difficult in this domain. High generalization performance and accelerated inference is crucial in building machine leaning models. For many complications, diseases, and conditions, a limited number of data samples is available which makes training of machine learning models challenging. It increases the risk of overfitting and lack of generalization performance in training models. In addition, due to many reasons such as privacy of patients and policies, many clinical sites are not able to share their data. Novel machine learning techniques must be development which are less prone to overfitting with high generalization performance. Data augmentation, synthesizing data, transfer learning, and federated learning are just some approaches to tackle these problems. In this special session, we are inviting authors to submit their original articles that address novel artificial intelligence and signal processing methods for tackling fundamental problems in data analytics, knowledge discovery, and meaningful use of complex medical data targeting important challenges in healthcare and digital medicine.

Biography of Organisers:

Arash Mohammadi (S’08-M’14-SM’17) received B.Sc. degree form ECE Department at University of Tehran, Tehran, Iran, in 2005, the M.Sc. degree from BME Department at Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2007, and Ph.D. degree from EECS Department at York University, in 2013. From 2013 to 2015, he was a Post-Doctoral Fellow at the Multimedia Lab, in the ECE Department, at the University of Toronto. He is currently an Associate Professor with Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada. Prior to joining Concordia University and for 2 years, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. He is a registered Professional Engineer in Ontario. During 2018–2021, he was the Director of Membership Developments of IEEE Signal Processing Society, the General Co-Chair of 2021 IEEE International Conference on Autonomous Systems, and the Guest Editor for the IEEE Signal Processing Magazine special issue on Signal Processing for Neurorehabilitation and Assistive Technologies. He is also an Associate Editor on the editorial board of IEEE Signal Processing Letters. He was the Co- Chair of the Symposium on Advanced Bio-Signal Processing and Machine Learning for Assistive and Neuro-Rehabilitation Systems as part of 2019 IEEE GlobalSIP, and Symposium on Advanced Bio-Signal Processing and Machine Learning for Medical Cyber- Physical Systems, as a part of IEEE GlobalSIP’18, The Organizing Chair of 2018 IEEE Signal Processing Society Video and Image Processing Cup, and the Lead Guest Editor of the IEEE Transactions on Signal & Information Processing over Networks special issue on Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems. He was the recipient of several distinguishing awards, including the Eshrat Arjomandi Award for outstanding Ph.D. dissertation from Electrical Engineering and Computer Science Department, York University, Toronto, ON, Canada, in 2013, Concordia President’s Excellence in Teaching Award in 2018, and 2019 and 2022 Gina Cody School of Engineering and Computer Science’s Research awards.

Hojjat Salehinejad (S’16-M’21-SM’22) received B.Sc. degree in Electrical Engineering from Shahid Bahonar University of Kerman, Kerman, Iran, in 2010, the M.A.Sc. degree in Electrical and Computer Engineering from University of Ontario Institute of Technology, Oshawa, ON, Canada, in 2014, and Ph.D. degree in Electrical and Computer Engineering from the University of Toronto, Toronto, ON, Canada in 2021. From 2021 to 2022, he was a Post-Doctoral Fellow at the department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. He is currently an Assistant Professor of Health Care Systems Engineering with the Mayo Clinic College of Medicine and Science and a Principal Investigator and Scientist with the Mayo Clinic in Rochester, MN, USA. He is the recipient of various awards and scholarships namely the prestigious Postdoctoral Fellowship Award in Artificial Intelligence from the Natural Sciences and Engineering Research Council of Canada (NSERC), Edward S. Rogers Sr. Graduate Scholarship, Ontario Trillium Scholarship, Mitacs Accelerated Fellowship, and Ontario Graduate Scholarship. He is the guest editor of the Journal of Imaging Special Issue on “Machine Learning for Human Activity Recognition” and publications Co- Chair of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2023). He has served as Technical Program Committee member of various IEEE conferences namely IEEE Conference on Communications – Machine Learning track, and IEEE World Congress on Computational intelligence. He is a reviewer for various journals namely IEEE Transactions on Medical Imaging, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Biomedical and Health Informatics, Journal of the American Medical Association (JAMA) and the proceedings of the National Academy of Sciences of the United States of America (PNAS). He is a Senior Member of IEEE and IEEE Signal Processing Society.