HassanRivaz

Hassan Rivaz

Concordia University

Abstract
Title:Machine Learning in Medical Ultrasound

This talk focuses on developing image analysis techniques that reveal otherwise hidden information in clinical ultrasound signals. Ultrasound is one of the most commonly used imaging modalities because of its low cost and ease of use. However, it has two main drawbacks. First, raw ultrasound data is not suitable for visualization, and as such, is converted to the familiar grey-scale images which leads to a loss of most of its information. Second, these grey-scale images are hard to interpret since they are noisy and collected at oblique angles. In this talk, we tackle these issues by developing techniques that extract clinically useful information such as tissue elasticity from the complex raw ultrasound signals, and register them to other modalities such as Magnetic Resonance Imaging (MRI) to help with their interpretation.

Bio

Hassan Rivaz received his BSc from Sharif University of Technology, MASc from UBC, and PhD from Johns Hopkins University. He is now an Associate Professor of ECE and a Concordia University Research Chair in Medical Image Analysis. He is an Associate Editor of IEEE Transactions Med Imag (TMI), and IEEE Transactions Ultras Ferroe Freq Cont (TUFFC). He has served as an Area Chair of MICCAI 2020, 2019, 2018 and 2017. He is a member of the organizing committee of IEEE IUS 2023 (Montreal, Canada), IEEE ISBI 2021 (Nice, France) and IEEE EMBC 2020 (Montreal, Canada). He co-organized the CuRIOUS MICCAI 2018 Challenge on correction of brain shift using ultrasound, and the CereVis MICCAI 2018 Workshop on Cerebral Data Visualization. He co-organized the advanced ultrasound imaging tutorial at IEEE ISBI 2019 (Venice, Italy) and 2018 (Washington, USA), and a workshop titled Machine Learning in Medical Ultrasound in ICIAR 2019 (Waterloo, Canada). He directs the IMPACT lab: IMage Processing and Characterization of Tissue, which can be found at https://users.encs.concordia.ca/~impact/