Professor, University of New South Wales, Australia;IEEE Fellow
Speech Title: Artificial Intelligence in Bioimage Analysis
Advanced biomedical imaging technologies play a key role in both healthcare and the life sciences, as they allow visualizing the structure and function of organs, tissues, cells, and even single molecules with very high sensitivity and specificity. Biomedical imaging devices typically generate vast amounts of multiparametric spatiotemporal imaging data, containing much more relevant and subtle information than can be processed by humans, even if they are experts. Hence there is an ever-growing need for computational methods to analyze these data automatically, not only to cope with the sheer volume of the image data sets, but also to reach a higher level of accuracy, objectivity, and reproducibility. To this end we develop advanced computer vision methods for a wide range of problems, including restoration, enhancement, super-resolution, and registration of images, as well as detection, segmentation, quantification, classification, and tracking of objects in these images. To cope with the high complexity of these problems, we rely increasingly on machine learning approaches for this, in particular deep learning using artificial neural networks. In addition to developing new methods, we are strong proponents of evaluating and benchmarking methods thoroughly and making them publicly available in the form of user-friendly software tools. This talk will highlight some of the methods we have been developing, especially for the analysis of cellular and intracellular dynamic processes and morphologies, to facilitate biological studies of the molecular mechanisms of life in health and disease.
Professor, Free University (VU), Amsterdam, The Netherlands
EiC of Journal of Artificial Intelligence for Medical Sciences
Professor Zhisheng Huang is a tenured senior researcher at the Department of Artificial Intelligence of VU University Amsterdam, the Netherlands, and a full professor at the School of Computer Science and Engineering of Wuhan University of Science and Technology, China. Prof. Huang obtained a doctorate degree in computer science and logic at the University of Amsterdam in 1994. He has published more than 200 papers, and more than 9 books in Artificial Intelligence, logics, and multimedia. He received the best paper award for the paper entitled “Feasibility Estimation for Clinical Trials” at the 2014 International Conference on Health Informatics (HEALTHINF/BIOSTEC).
Associate Professor, School of Medicine, Bologna University, Italy
Pier Paolo Piccaluga, MD, Ph.D. is currently an Associate Professor of Pathology at the Department of Experimental, Diagnostic and Specialty Medicine, Bologna University School of Medicine—Institute of Hematology and Medical Oncology, and executive physician at The Biobank of Research, IRCCS S. Orsola-Malpighi Hospital. In 2018, he was appointed to teaching posts at the Queen Mary University of London and Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. He is the author of several international publications in journals such as Nature Medicine, Journal of Clinical Investigation, Journal of Experimental Medicine, Journal of Clinical Oncology, Blood, Lancet Oncology, and Lancet Infectious Diseases. Dr. Piccaluga is ranked a Top Italian Scientist (TIS) by VIA-Academy.
Professor, Computational Medical Imaging Laboratory, Sun Yat-sen University, China