Past Events

Artificial Intelligence (AI) applied to medical images has the potential to transform patient care, especially in radiation oncology where radiological imaging is ubiquitous and treatment planning is time consuming.
AI models are data hungry but medical data are often insufficient for many applications. Building foundational models and fine-tuning them for specific medical tasks with only a few data points represents a new avenue of research with high translational potential. However, there is a lack of large compendia of radiological data that are sufficiently curated for building these foundational models.
In this session, the presenters will describe current work on implementing Med-ImageNet, a compendium of radiological images that have been standardised to develop deep learning models, such radiomics predictors or the foundational MedSAM model.

For the May forum on machine learning in computational pathology, the first presentation by April Khademi, a researcher, will be about the past, present and future of digital pathology and AI. The second presentation by Phedias Diamandis, a pathologist, will be about approaches to solve implementation challenges of AI in the real world. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

For the April forum on machine learning in precision health, the first presentation by Ross Mitchell, a Professor of Medicine and AI scientist at the University of Alberta, will be about AI and Precision Health. The second presentation by Jacob Jaremko a radiologist, entrepreneur and AI scientist, will be about AI augmented ultrasound imaging. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

For the January forum on machine learning in image reconstruction, the first presentation by Ge Wang, a researcher, will be about CT image reconstruction and the second presentation by Charlie Millard, a postdoctoral fellow, will be about MRI image reconstruction. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Douglas Lee, a cardiologist and clinician scientist, will present his work on machine learning in COVID-related risk analysis using large-scale population data. Simon Graham, a senior scientist, will present his recent work on COVID-related medical imaging. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Richard Do, a radiologist, will start the forum with a presentation on natural language processing (NLP) of radiology reports to track metastatic spread. Heidi Hanson, a researcher, will talk about NLP in cancer surveillance. The two presentations will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Jaron Chong will start the forum with an overview of the state of AI for medical imaging in Canada. The second presentation will be by Alex Bilbily talking about the road to building impactful AI medical devices. The third presentation will be by Benjamin Fine on pre-deployment evaluation. The three talks will be followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.