ICBET 2022          Full Paper Submission Deadline: 20 November, 2021     Conference Dates: 20th - 23th April 2022      Conference Place: Tokyo, Japan



Tokyo, Japan





Keynote Speaker I

Prof. Walter Herzog

University of Calgary, Canada

Dr. Herzog did his undergraduate training in Physical Education at the Federal Technical Institute in Zurich, Switzerland (1979), completed his doctoral research in Biomechanics at the University of Iowa (USA) in 1985, and completed postdoctoral fellowships in Neuroscience and Biomechanics in Calgary, Canada in 1987. Currently, Dr. Herzog is a Professor of Biomechanics with appointments in Kinesiology, Medicine, Engineering, and Veterinary Medicine, holds the Canada Research Chair for Cellular and Molecular Biomechanics, and is appointed the Killam Memorial Chair for Inter-Disciplinary Research at the University of Calgary. His research interests are in musculoskeletal biomechanics with emphasis on mechanisms of muscle contraction focusing on the role of the structural protein titin, and the biomechanics of joints focusing on mechanisms of onset and progression of osteoarthritis. Dr. Herzog is the recipient of the Borelli Award from the American Society of Biomechanics, the Career Award from the Canadian Society for Biomechanics, the Dyson Award from the International Society of Biomechanics in Sports, the Muybridge Award from the International Society of Biomechanics, and recently received the Killam Prize in Engineering from the Canada Council for the Arts for his contributions to Biomedical research. He is the past president of the International, American and Canadian Societies for Biomechanics. He was inducted into the Royal Society of Canada in 2013.

Speech Title: "Recent Observations on the Molecular Mechanisms of Muscle Contraction"

Abstract: Eccentric muscle contractions are not well captured by the cross-bridge theory of muscle contraction. Specifically, the permanent extra force obtained after stretching an active muscle cannot be explained with current thinking. We discovered that aside from the contractile proteins, actin and myosin, the filamentous protein titin is also involved in active force regulation. Using genetic modifications of titin, anti-body labeling of titin within myofibrils, and first ever mechanical measurements on mechanically isolated sarcomeres, we identified how titin functions in situ in skeletal and cardiac muscles. We found that elongation of titin in active and passive muscle differ. In passive muscle, the entire filament is elongating upon muscle stretching, while in active muscle only the distal elements elongate; the proximal segments remain at a constant length. Furthermore, genetic deletion of the N2A segment changes titin’s in situ function dramatically. Finally, titin works similarly in cardiac and skeletal muscle, despite vastly different isoforms and reports in the literature to the contrary. We conclude that titin is responsible for the extra permanent force following elongation of active muscles in cardiac and skeletal tissues and suggest that titin does this by binding its proximal segments to actin upon muscle activation.

Keynote Speaker II

Prof. Irene Yu-Hua Gu

Chalmers University of Technology, Sweden

Dr. Irene Yu-Hua Gu received Ph.D. degree in electrical engineering from Eindhoven University of Technology, Eindhoven, The Netherlands, in 1992. From 1992 to 1996, she was Research Fellow at Philips Research Institute IPO, Eindhoven, The Netherlands, post dr. at Staffordshire University, Staffordshire, U.K., and Lecturer at the University of Birmingham, Birmingham, U.K. Since 1996, she has been with the Department of Electrical Engineering (previous name: Department of Signals and Systems), Chalmers University of Technology, Gothenburg, Sweden, where she has been a Professor since 2004. Her research interests include statistical image and video processing, video object tracking and recognition, machine learning and deep learning, and signal processing with applications. During the last several years her main research has been focused on biomedical image analysis and deep learning. Dr. Gu was an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, and Part B: Cybernetics from 2000 to 2005, and Associate Editor for EURASIP Journal on Advances in Signal Processing from 2005 to 2016, Editorial Board of the Journal of Ambient Intelligence and Smart Environments from 2011 to 2019. She was elected as the Chair of the IEEE Swedish Signal Processing Chapter from 2001 to 2004. She is a senior member of IEEE. She has coauthored over 200 papers, and has been ranked as the top 50 scientists in the field of Computer Science and Electronics in Sweden in the 6th Edition of 2020 ranking by Guide2Research team.

Speech Title: "Deep Learning Methods for Assisting Medical Diagnosis of Neurological Diseases: Some Methods, Possibilities and Challenges"

Abstract: Recent developments in imaging technology have revolutionized healthcare. Medical images are widely used for diagnosis and therapy monitoring with significant impact on patient treatment outcome. Despite these advances, routine clinical MRI data interpretation is still performed mostly by human experts. There has been an impressive progress in deep learning, however, deep learning-assisted medical diagnosis of neurological diseases remains a challenged research area. In this talk, we will describe several deep learning methods on MR images. These methods show great potentials, for example, in computer-assisted diagnosis of Alzheimer’s disease, or prediction of brain tumor molecular subtypes without biopsy. We also discuss common challenges when dealing with medical datasets, for example, combination of several small medical datasets, incomplete MRI data of individual patients, or, partly annotated training datasets. These issues may impact the training process, leading to low generalization performance on the test data. We then describe several possible solutions to these challenging issues, including domain mapping for combining small training datasets, MRI data augmentation for generating missing data and fake patients’ data. Finally, we demonstrate that these methods are rather effective and may offer great potentials for future research in this area.


To be added.


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