Third Palestinian International Conference on Information and
Communication Technology (PICICT 2021)
(The proceedings will be submitted to IEEE)
September 28-29, 2021
Faculty of Information Technology, IUG
Faculty of Computers and Information Technology, AQU.
Gaza, Palestine
Keynote speakers
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Dr. Shadi Albarqouni |
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Associate Professor |
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Helmholtz AI Young Investigator Group Leader at Helmholtz Center Munich TUM Junior Fellow at Technical University Munich, Germany |
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Speech Title: Towards Deep Federated Learning in Healthcare |
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Abstract: Deep Learning (DL) has emerged as a leading technology in computer science for accomplishing many challenging tasks. This technology shows an outstanding performance in a broad range of computer vision and medical applications. However, this success comes at the cost of collecting and processing a massive amount of data, which are in healthcare often inaccessible due to privacy issues. Federated Learning is a new technology that allows training DL models without sharing the data. Using Federated Learning, DL models at local hospitals share only the trained parameters with a centralized DL model, which is, in return, responsible for updating the local DL models as well. Yet, a couple of well-known challenges in the medical imaging community, e.g., heterogeneity, domain shift, scarify of labeled data and handling multi-modal data, might hinder the utilization of Federated Learning. In this talk, a couple of proposed methods, to tackle the challenges above, will be presented paving the way to researchers to integrate such methods into the privacy-preserved federated learning. |
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Shadi Albarqouni is an AI Young Investigator Group Leader at Helmholtz AI and TUM Junior Fellow at TU Munich. Previously, he worked as a Visiting Scientist at Imperial College London and ETH Zurich, and as a Senior Research Scientist & Team Lead at the Technical University of Munich (TUM). Shadi has more than 100 publications (Citations > 2500, h-index: 16) in Medical Imaging Computing, Computer Vision, and Machine Learning published in MedIA, IEEE TMI, IEEE JBHI, IPMI, MICCAI, MIDL, ISBI, ICCV, ECCV, BMVC, and ICML. He serves as a reviewer for many journals such as MedIA, IEEE TMI, IEEE JBHI, and Nature Communication. Shadi has served as a PC and OC member for a couple of MICCAI and NeurIPS workshops. Since 2019, he has been serving as an Area Chair at MICCAI. His current research interests include Interpretable ML, Robustness, Uncertainty quantification, and recently Federated Learning. He is also interested in Entrepreneurship and Startups for Innovative Medical Solutions, in particular, knowledge transfer to developing and emerging countries. Details can be found here: https://albarqouni.github.io/ |
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Professor |
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Computer Science, Birzeit University |
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