Prof. Carlos M. Travieso-González

Universidad de Las Palmas de Gran Canaria, Spain

Carlos M. Travieso-González received the M.Sc. degree in 1997 in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain; and Ph.D. degree in 2002 at University of Las Palmas de Gran Canaria (ULPGC-Spain). He is Full Professor on Signal Processing and Pattern Recognition and Head of Signals and Communications Department at ULPGC; teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 51 International and Spanish Research Projects, some of them as head researcher. He is co-author of 4 books, co-editor of 25 Proceedings Book, Guest Editor for 8 JCR-ISI international journals and up to 24 book chapters. He has over 440 papers published in international journals and conferences He has published 7 patents in Spanish Patent and Trademark Office. He has been supervisor on 8 PhD Thesis (12 more are under supervision), and 130 Master Thesis. He have been General Chair in 12 international conferences. He is evaluator of project proposals for European Union (H2020), Medical Research Council (MRC – UK), Spanish Government (ANECA - Spain), Research national Agency (ANR - France), DAAD (Germany), Argentinian Government and Colombian Institutions. He won “Catedra Telefonica” Awards in Modality of Knowledge Transfer, in the editions 2017, 2018 and 2019.

Speech Title: "Analysis of New Diagnostic Test Technologies for Neurodegenerative Diseases"

Abstract: Neurodegenerative diseases, such as Alzheimer's and Parkinson's, represent a major public health challenge due to their impact on patients' quality of life and their increasing prevalence in today's society. In recent years, the development of advanced imaging techniques has opened up new opportunities for early and accurate diagnosis of these diseases. These techniques allow the extraction and analysis of specific features in medical images, such as MRI and PET scans, which can reveal subtle changes in the brain associated with neurodegenerative diseases. This proposal will demonstrate a low-cost test that can facilitate its use in society, based on facial images. The use of artificial intelligence algorithms has shown particular promise in detecting and classifying abnormal patterns in the brain. These methods can identify information in facial images, and relate it to neurodegenerative diseases, facilitating early detection and accurate monitoring of disease progression.

Prof. Hiroshi Fujita

Gifu University, Japan

Prof. Hiroshi Fujita received Ph.D. degree from Nagoya University in 1983. He was a visiting researcher at the K.Rossmann Radiologic Image Laboratory, University of Chicago, in 1983-1986. He became an associate professor in 1991 and a professor in 1995 in the Faculty of Engineering, Gifu University. He has been a professor and chair of intelligent image information since 2002 at the Graduate School of Medicine, Gifu University. He is now a Research Professor of Gifu University. He is a member of the Society for Medical Image Information (Honorary President), the Institute of Electronics, Information and Communication Engineers (IEICE, Fellow), and the Japan Society for Medical Imaging Technology (Honorary member). His research interests include computer-aided diagnosis system, image analysis/processing/evaluation in medicine. He received numerous awards such as the Medical Imaging Information Society Award (2018), RSNA (2001, 6 others), SPIE (1995, 8 others), etc. He has co-published over 1000 papers in Journals, Proceedings, Book chapters and Scientific Magazines.

Speech Title: "Exploring the Latest Fundamentals of AI-based Computer-Aided Diagnosis (AI-CAD) in Medical Imaging"

Abstract: Computer-aided diagnosis, or CAD, of medical image is rapidly becoming the mainstream of practical medicine; in CAD, the computer output is used as a "second opinion" for doctors to interpret images. However, recent powerful AI technologies, including deep learning, have taken CAD development and performance to the next level, diversifying traditional CAD and even introducing autonomous diagnostic AI. This is sometimes referred to as AI-CAD, which is gradually moving from the mere R&D level to a commercialization level, verification at the actual clinical stage, and insurance reimbursement stage. In this lecture, we would like to review the current status of AI-CAD and discuss issues that need to be resolved in order to make AI-CAD practical in clinical practice. We hope that many audiences will become more interested in medical imaging AI-CAD and engage in research and development in this field through this lecture.