Medical image analysis using deep learning

Oge Marques, Florida Atlantic University
Host: Haider Ali

The field of medical image analysis has experienced a significant shift towards deep learning approaches, particularly convolutional neural networks (CNNs), in recent years. Thanks to their ability to learn features, representations, and tasks directly from images – thereby eliminating the need for manual feature extraction and selection – deep learning solutions are becoming ever more prevalent.

In this talk I will present an overview of recent and ongoing collaborative work in topics related to medical image analysis using deep learning, particularly: (1) tuberculosis type classification from CT scans; (2) skin lesion segmentation and classification; and (3) surgery video summarization and content analysis.

Speaker Biography

Oge Marques, PhD is Professor of Computer Science and Engineering at the College of Engineering and Computer Science at Florida Atlantic University (FAU) (Boca Raton, Florida, USA).

He is Tau Beta Pi Eminent Engineer, ACM Distinguished Speaker, and the author of more than 100 publications in the area of intelligent processing of visual information – which combines the fields of image processing, computer vision, image retrieval, machine learning, serious games, and human visual perception –, including the textbook “Practical Image and Video Processing Using MATLAB” (Wiley-IEEE Press).

Professor Marques is Senior Member of both the IEEE (Institute of Electrical and Electronics Engineers) and the ACM (Association for Computing Machinery) and member of the honor societies of Sigma Xi, Phi Kappa Phi and Upsilon Pi Epsilon. He has more than 30 years of teaching and research experience in different countries (USA, Austria, Brazil, France, India, Spain, Serbia, and the Netherlands).