October 07, 2023, 9:00 am to 1:00 pm

Pre-MICCAI

 Workshop@UBC

 

Overview

The Pre-MICCAI Workshop is a dynamic and innovative platform that unites the realms of machine learning and medical computer vision. As a prelude to the prestigious MICCAI (Medical Image Computing and Computer Assisted Intervention) conference, this workshop serves as a vital nexus where experts, researchers, and enthusiasts converge to explore cutting-edge advancements, exchange knowledge, and foster collaborative partnerships in the field of medical image analysis. We welcome you to join us at the University of British Columbia University (UBC), Vancouver Campus. Please register via the following bottun. 

 

Speakers and Panelists

Ali Bashashati

UBC

Ruogu Fang

University of Florida

Hervé Lombaert

ETS Montreal

Shaoting Zhang

Shanghai AI Lab

Jun Ma

University of Toronto/Vector/UHN

 

Briefings

Sana Ayromlou

UBC Student

Beidi Zhao

UBC Student

Hooman Vaseli UBC Student

 

Workshop Program

9:00 - 9:05

Opening

9:05 - 9:30

Keynote Talk 1 - Machine Learning Opportunities in Computational Pathology 

Ali Bashashati

9:30 - 10:00

Keynote Talk 2 - Foundation Models in Medicine: Generalist vs Specialist 

Shaoting Zhang

10:00 - 10:15

Break

10:15 - 10:30

Briefings

10:30 - 11:00

Keynote Talk 3 - Geometric Deep Learning - Examples on Brain Surfaces 

Hervé Lombaert

11:00 - 11:30

Keynote Talk 4 - A Tale of Two Frontiers: When Brain Meets AI 

Ruogu Fang

11:30 - 11:50

Panel Discussion

Ali Bashashati/Ruogu Fang/Shaoting Zhang/Hervé Lombaer/Jun Ma (Moderator: Xiaoxiao Li)

11:50 - 12:00

Closing

12:00 - 13:00

Lunch/Demo/Campus Tour

 

Organizers

Assist. Prof

UBC/Vector/BMIAI

Professor

UBC/BMIAI

Professor

UBC/BMIAI

SangMook Kim

Postdoc

UBC/Vector

The Venue

Pre-MICCAI Workshop


Fred Kaiser Building (KAIS) 2020-2030

2332 Main Mall, Vancouver, BC V6T 1Z4


              Acknowledgement 

We would like to acknowledge that we are gathered today on the traditional, ancestral, and unceded territory of the Musqueam people.