Keynote Speakers

Prof. Tayeb A. Denidni (IEEE Fellow), received M. Sc. and Ph.D. degrees in electrical engineering from Laval University, Quebec City, QC, Canada, in 1990 and 1994, respectively.

From 1994 to 2000, he was a professor with the engineering department, Université du Quebec in Rimouski (UQAR), Rimouski, QC, Canada. Since August 2000, he has been with the Institut National de la Recherche Scientifique (INRS), Université du Quebec, Montreal, QC, Canada. He founded RF laboratory at INRS-EM, Montreal. He has a great experience with antenna design. He served as a principal investigator on many research projects sponsored by NSERC and numerous industries. He has co-authored more than 250 journal papers, 300 conference papers, 1 book and 9 book chapters. He has acquired international recognition for its innovative and pioneering work on metamaterials, electromagnetic periodic structures and their applications, beamforming antenna systems, adaptive arrays, millimeter-wave antennas and dielectric resonator antennas. He has been elevated to the grade of IEEE fellow for his contribution to frequency selective surfaces and their applications to reconfigurable antennas

From 2008 to 2010, Dr. Denidni served as an Associate Editor for IEEE Transactions on Antennas Propagation. From 2005 to 2007, Dr. Denidni served as an Associate Editor for IEEE Antennas Wireless Propagation Letters. Since 2015, he has been serving as an Associate Editor for IET Electronics Letters.

Recently, advanced beamforming antennas using engineered electromagnetic materials, such as FSS, EBG and metamaterials, are one of the attractive research topics, which have received much interest from many research and industrial groups worldwide. Using engineered electromagnetic materials, novel antenna systems with beamforming capability can be developed and used us an enabling technology to improve the performance of future wireless communication, radar and space systems at microwave and mm-wave bands. These approaches are based on using advanced artificial periodic electromagnetic structures, including electromagnetic band gap (EBG) structures, frequency selective surfaces (FSS), matasurfeces and metamaterials. The objective is to design and implement new compact, low profile, and low-cost antenna systems with high performances in terms of beamforming capability, high gain, and efficiency. With these features, they can be used to improve the performances of future wireless communication systems. For instance, they allow saving energy, increasing the coverage area, and reducing interference, which lead to good transmission quality. In this talk, I will first give a brief introduction on the wireless communication systems, presenting their potentials and challenges. Second, I will give an overview on periodic electromagnetic structures and their applications in advanced antenna designs. Third, I will present a few configurations of FSS-based reconfigurable antennas using various electromagnetic periodic structures. To show the beamforming feature of these antennas, some examples of simulated and experimental results will be presented and discussed. Finally, concluding remarks will be given.

Full Prof. Amir Nakib received the M.D. degree in electronic and image processing from University Paris 6, in 2004, the Ph.D. degree in computer science from the Université Paris 12, France, in December 2007, and the Habilitation degree in computer science from University Paris East, in December 2015. Then, he has got a position as the Research Head at Logxlabs Company, where he worked on the design of innovative methods for solving different real world problems. Since September 2010, he has been an Laboratoire LISSI, University Paris Est Créteil, where he does research on learning-based optimization for computer vision, and network problems. He has published more than 200 publications in deep learning, computer vision and optimization. Currently he is full professor and Head of AI team.

Deep neural network (DNN) models are often very large, which makes them computationally and memory intensive. However, many real-world problems require fast inference times. For example, machine vision applications demand real-time performance, with dozens of samples requiring inference every second. Additionally, many other applications rely on cloud inference computing, which can lead to overwhelming costs. The computational barrier during inference presents a significant challenge for DNNs to handle real-world use cases. In this talk, the focus will be on exploring different approaches to improve the computational performance and memory requirements of DNN models during the inference process.

Prof. Habib Zaidi, Ph.D, PD, FIEEE, FAIMBE, FAAPM, FIOMP, FAAIA, FBIR email: habib.zaidi@hcuge.ch Web: http://www.pinlab.ch/ Habib Zaidi is Chief physicist and head of the PET Instrumentation & Neuroimaging Laboratory at Geneva University Hospital and full Professor at the medical school of Geneva University. He is also a Professor of Medical Physics at the University of Groningen (Netherlands), Adjunct Professor of Medical Physics and Molecular Imaging at the University of Southern Denmark, Adjunct Professor of Medical Physics at Shahid Beheshti University visiting Professor at Tehran University of Medical Sciences and Distinguished Adjunct Professor at King Abdulaziz University, KSA. He is actively involved in developing imaging solutions for cutting-edge interdisciplinary biomedical research and clinical diagnosis in addition to lecturing undergraduate and postgraduate courses on medical physics and medical imaging. His research is supported by the EEC, Swiss National Foundation, EEC, private foundations and industry (Total 8.8 M US$) and centres on hybrid imaging instrumentation (PET/CT and PET/MRI), deep learning for various imaging applications, modelling medical imaging systems using the Monte Carlo method, development of computational anatomical models and radiation dosimetry, image reconstruction, quantification and kinetic modelling techniques in emission tomography as well as statistical image analysis, and more recently on novel design of dedicated PET and PET/MRI scanners. He was guest editor for 13 special issues of peer-reviewed journals dedicated to Medical Image Segmentation, PET Instrumentation and Novel Quantitative Techniques, Computational Anthropomorphic Anatomical Models, Respiratory and Cardiac Gating in PET Imaging, Evolving medical imaging techniques, Trends in PET quantification (2 parts), PET/MRI Instrumentation and Quantitative Procedures and Clinical Applications, Nuclear Medicine Physics & Instrumentation, and Artificial Intelligence and serves as founding Editor-in-Chief (scientific) of the British Journal of Radiology (BJR)|Open, Deputy Editor for Medical Physics, and member of the editorial board of the Journal of Nuclear Cardiology, Physica Medica, International Journal of Imaging Systems and Technology, Clinical and Translational Imaging, American Journal of Nuclear Medicine and Molecular Imaging, Brain Imaging Methods (Frontiers in Neuroscience & Neurology), Cancer Translational Medicine and the IAEA AMPLE Platform in Medical Physics. He has been elevated to the grade of fellow of the IEEE, AIMBE, AAPM, IOMP, AAIA and the BIR and was elected liaison representative of the International Organization for Medical Physics (IOMP) to the World Health Organization (WHO) and Chair of Subcommittee on Part 1 Examination of the International Medical Physics Certification Board (IMPCB) and the Imaging Physics Committee of the AAPM in addition to being affiliated to several International medical physics and nuclear medicine organisations. He is developer of physics web-based instructional modules for the RSNA and Editor of IPEM’s Nuclear Medicine web-based instructional modules. He is involved in the evaluation of research proposals for European and International granting organisations and participates in the organisation of International symposia and conferences. His academic accomplishments in the area of quantitative PET imaging have been well recognized by his peers and by the medical imaging community at large since he is a recipient of many awards and distinctions among which the prestigious 2003 Bruce Hasegawa Young Investigator Medical Imaging Science Award given by the Nuclear Medical and Imaging Sciences Technical Committee of the IEEE, the 2004 Mark Tetalman Memorial Awardgiven by the Society of Nuclear Medicine, the 2007 Young Scientist Prize in Biological Physics given by the International Union of Pure and Applied Physics (IUPAP), the prestigious (100’000$) 2010 kuwait Prize of Applied sciences (known as the Middle Eastern Nobel Prize) given by the Kuwait Foundation for the Advancement of Sciences (KFAS) for "outstanding accomplishments in Biomedical technology", the 2013 John S. Laughlin Young Scientist Award given by the AAPM, the 2013 Vikram Sarabhai Oration Award given by the Society of Nuclear Medicine, India (SNMI), the 2015 Sir Godfrey Hounsfield Award given by the British Institute of Radiology (BIR), the 2017 IBA-Europhysics Prize given by the European Physical Society (EPS)and the 2019 Khwarizmi International Award given by the Iranian Research Organization for Science and Technology (IROST). Prof. Zaidi has been an invited speaker of over 160 keynote lectures and talks at an International level, has authored over 840+ publications (he is the senior or first author in a majority of these publications), including 373 peer-reviewed journal articles in high ranking journals, most of them in Q1/D1 of their categories (ISI-h index=55|73Web of Science™|Google scholar, >19’050+ citations), 425conference proceedings and 42 book chapters and is the editor of four textbooks on Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine (2 Editions), Quantitative Analysis in Nuclear Medicine Imaging, Molecular Imaging of Small Animals and Computational anatomical animal models.

Positron emission tomography (PET), x-ray computed tomography (CT) and magnetic resonance imaging (MRI) and their combinations (PET/CT and PET/MRI) provide powerful multimodality techniques for in vivo imaging. This talk presents the fundamental principles of multimodality imaging and reviews the major applications of artificial intelligence (AI), in particular deep learning approaches, in multimodality medical imaging. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. To this end, the applications of deep learning in five generic fields of multimodality medical imaging, including imaging instrumentation design, image denoising (low-dose imaging), image reconstruction quantification and segmentation, radiation dosimetry and computer-aided diagnosis and outcome prediction are discussed. Deep learning algorithms have been widely utilized in various medical image analysis problems owing to the promising results achieved in image reconstruction, segmentation, regression, denoising (low-dose scanning) and radiomics analysis. This talk reflects the tremendous increase in interest in quantitative molecular imaging using deep learning techniques in the past decade to improve image quality and to obtain quantitatively accurate data from dedicated standalone (CT, MRI, SPECT, PET) and combined PET/CT and PET/MRI imaging systems. The deployment of AI-based methods when exposed to a different test dataset requires ensuring that the developed model has sufficient generalizability. This is an important part of quality control measures prior to implementation in the clinic. Novel deep learning techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical medical imaging community. Future opportunities and the challenges facing the adoption of deep learning approaches and their role in molecular imaging research are also addressed.

Prof. Aydin received B.Sc. (1984) and M.Sc. (1987) degrees in Electronics and Communication Engineering at Yildiz Technical University, Turkiye, and Ph.D. degree in Medical Physics (1994) at the University of Leicester, UK. He worked in the Department of Clinical Neuroscionces at Kings College London and the Division of Clinical Neuroscience at St George's Hospital Medical School as a Research Fellow between 1998 and 2001. He was a Senior Research Fellow in the Institute for Integrated Micro and Nano Systems at the University of Edinburgh from 2001 to 2004. In 2004, he was appointed as head of Computer Engineering Department and in 2006 he founded Software Engineering Department, both at Bahcesehir University, Turkiye. Between 2009 and 2023, he was professor in the Computer Engineering Department at Yildiz Technical University and served as the head of the department between 2011 and 2023. He is now with the Computer Engineering Department at Istanbul Technical University, Türkiye. His research interests and contributions cover a wide range of subjects including biomedical signal/image processing, bioinformatics, system-on-chip, AI, data science, and computer science. He was awarded the IEE (now IET) the Institute Premium Award for 2000/2001 for his contributions in complex wavelet transform for processing Doppler ultrasound signals. He is a senior member of IEEE and acts as IEEE Türkiye Section Chair.

Infertility is a disease of the male or female reproductive system defined by the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse (WHO, 2018). Today, infertility has become a common problem affecting approximately 20% of the world's population. Infertility can be caused by male or female factors. The diagnostic process for infertility is different for men and women. When diagnosing infertility, reproductive cells of men and women are examined separately. In the diagnosis of male infertility factors, analysis of sperm cells is carried out under certain conditions in a laboratory setting. When analyzing sperm cells, three important features of sperm are used: morphology, concentration, and motility. Sperm analysis can be done visually by doctors or by using computer aided sperm analysis systems. The importance of computer-assisted analysis is increasing day by day because visual examination gives different results from person to person and is costly. On the other hand, computer-based expert systems are more consistent and reliable. However, they are not available in many laboratories because they are very expensive. A better solution is to adapt a hybrid expert system that combines computerized analysis and the visual evaluation environment to eliminate the disadvantages of both approaches. In this talk, approaches utilizing concepts from computer vision, artificial intelligence, image/video processing, and scientific computing and their evaluations by using sperm morphology datasets will be presented.