News

Rohan Khan Talks about Algorithmic Bias and Fairness

In a recent seminar organized by the Queen's School of Computing as part of The Social, Ethical, and Legal Issues in Computing Lecture Series, Rohan Khan, a Med-i CREATE doctoral student, talked about the intricate realm of algorithmic bias and fairness. Her presentation, titled "Algorithmic Bias and Fairness: Exploring Historical Context, Methodological Shortcomings, and Future Challenges," looked closely at important...
Zhendong Sha Successful Ph.D. Defense

Zhendong Sha Successful Ph.D. Defense

Zhendong Sha, a Med-i CREATE student completed his Ph.D. defense in December 2023. He was supervised by Dr. Ting Hu. Congratulations Zhendong! Visit MIB@Queen's for information
Inspiring Connection: Dr. Wolfgang Wein Engages with Med-i CREATE Students

Inspiring Connection: Dr. Wolfgang Wein Engages with Med-i CREATE Students

On November 30, Dr. Wolfgang Wein, Founder and CEO of ImFusion, delivered a research talk at the Queen's School of Computing as part of the Med-i CREATE Lecture series titled "Meet the Industry Leader." Following his talk, Med-i CREATE students had the chance to engage in a meet-and-greet session with Dr. Wein. This provided an opportunity for those interested to...
MICCAI 2023: Med-i CREATE student receives awards

MICCAI 2023: Med-i CREATE student receives awards

Laura Connolly, a Med-i CREATE student won the Best Demo Award at the 4th International Workshop of Advances in Simplifying Medical UltraSound (ASMUS) - which is a workshop held in conjunction with Medical Image Computing and Computer Assisted Interventions (MICCAI) 2023. She also won an Outstanding Reviewer Awards for the main MICCAI conference.  Well done Laura! Laura Connolly (third from...

Projects

Integration, federation, and retrieval of large-scale data repositories in Canada

We collaborate with the Canadian Institute of Health Information (CIHI) and the Ontario Health Data Platform (OHDP) to tackle novel challenges in data management and devise evaluation frameworks that center on security, performance and fairness.

Next generation of actionable prescriptive analysis using multi-resolution, multi-modality data

We build computation models of disease from multi-omics data using machine learning, deep learning and evolutionary algorithms, focusing on innovations that address the unique nature of health data and the unavailability and vagueness of gold-standard labels (e.g., pathology labels).

Democratization of AI, software and data for healthcare

We're international leaders in developing and disseminating open source software using low-cost point-of-care imaging. Our positive impact on the global computer–assisted medical interventions community has is constantly expanding. We'll meet new challenges for discovery, refinement of methods and innovative AI-powered solutions using large open source data, hence democratizing access to care and positive outcomes of AI.