Professor Oge Marques
Department of Computer and Electrical Engineering & Computer Science, Florida Atlantic University
Boca Raton, Florida
- Title of the tutorial:
Medical Image Analysis Using Deep Learning
The field of artificial intelligence (AI) – particularly machine learning – has experienced significant growth during the past 20 years, and many intelligent systems for medical diagnosis have been developed both in industry and academia. More recently, the emergence of a machine learning paradigm known as deep learning has further energized the field and enabled the development of medical image analysis systems that can display remarkable accuracy, to the point of raising concerns about the future of human experts, such as radiologists and anatomical pathologists.
In this tutorial we provide an extensive coverage of the impact of machine learning on medical image analysis, with emphasis on the increasingly popular deep learning techniques currently being used to design and implement intelligent medical imaging-based diagnosis systems.
This tutorial includes the following main topics: (1) Machine learning (ML): fundamentals, tools, and best practices; (2) Deep learning: fundamentals, success stories, and frameworks; (3) Medical image analysis tools and techniques; (4) Medical image analysis using ML and deep learning: examples, datasets, benchmarks, and challenges.
- Short biography:
Dr. Oge Marques is a Professor of Engineering and Computer Science at Florida Atlantic University. His research has been focused on the intelligent processing of visual information, which encompasses the fields of image processing, computer vision, human vision, artificial intelligence and machine learning. He is the author of nine technical books, one patent, and more than 100 scientific articles in his fields of expertise. He has more than 30 years of teaching experience in different countries (USA, Austria, Brazil, Netherlands, Spain, France, and India). Professor Marques is ACM Distinguished Speaker, Tau Beta Pi Eminent Engineer, and a Senior Member of both the IEEE (Institute of Electrical and Electronics Engineers) and the ACM (Association for Computing Machinery). He is also a member of the honor societies of Sigma Xi, Phi Kappa Phi and Upsilon Pi Epsilon and the recipient of several teaching awards.