Past Events

April 25, 2025 from 3:00 to 4:30 p.m. ET
Glenn Bauman and Katherine Zukotynski will talk at the April MaLMIC forum about FULCRUM, a clinical imaging database they created. It is a database of PSMA PET/CT scans acquired as part of a province-wide prospective study. It is composed of approximately 1000 men with recurrent prostate cancer imaged with 18F-DCFPyL or 18F-PSMA 1007 with centralized expert review, annotation and segmentation of PET-detected recurrent prostate cancer foci. The database is being used to design educational tools and enable machine learning/deep learning tools for automated lesion detection.

MaLMIC is pleased to support the 2025 IGT x ImNO Joint Symposium in Toronto ON on March 5 and 6, 2025. Throughout the symposium, there will be talks, pitches and posters on AI, deep learning and machine learning.

January 31, 2025
3:00 to 4:30 p.m. Eastern

Gordon Harris and Alireza Sedghi presented information about the Open Health Imaging Foundation (https://ohif.org/), an open source web based medical imaging framework, at the January forum. The presentations were followed by questions and answers about the talks, and discussions focused on lessons learned, opportunities to collaborate, and sharing of data.

October 25, 2024
3:00 to 4:30 p.m. Eastern

Aly Khalifa and Sanwook Kim will present at the MaLMIC fall forum, hosted by Julia Publicover. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

September 20, 2024
3:00 to 4:30 p.m. Eastern

Elham Dolatabadi and Catherine Stinson will present at the MaLMIC fall forum, hosted by Amber Simpson. The presentations wil be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Join us for an engaging webinar on June 27th at 11:30am that showcases the Quantitative Imaging for Personalized Cancer Medicine (QIPCM) program which provides end-to-end testing and analysis support for clinical trials to improve consistency and reliability in clinical trial imaging data.

MaLMIC is pleased to support the ImNO 2024 symposium in Mississauga ON on March 19 and 20, 2024. Here are some of the sessions featuring AI and machine learning:
– Keynote: Anne Martel will talk about AI for medical image analysis: Living with limited data
– Debate: The role of AI in medicine
– Panel: Commercialization of AI-enabled medical imaging technology
Throughout the symposium, there will also be talks, pitches and posters on AI, deep learning and machine learning.
We hope you can join us at ImNO 2024!

This forum will focus on computational pathology for outcome prediction in cancer. Mattias Rantalainen will be the first speaker talking about his research program and the work of Stratipath predictive models that are being translated into the clinic. Faisal Mahmood, will be the second speaker talking about the work in his lab that uses machine learning, data fusion and medical image analysis to develop streamlined workflows for objective diagnosis, prognosis, and biomarker discovery. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

This forum will focus on translating AI-based innovations in medicine into the commercial world and the clinic. The first speaker, Qian Cao, will talk about developing AI-enabled software as a medical device and the FDA Centre for Devices and Radiological Health’s perspective on AI/ML devices. Our second speaker, Eli Gibson, will talk about the complexities of translating AI/ML ideas from a manufacturer’s perspective and showcase examples. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

The October forum will focus on using machine learning to predict disease progression. Su-In Lee, will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

The MaLMIC fall forum, hosted by Dr. Lincoln Stein, will focus on human sample and data resources for machine learning research. Dr. Dianne Chadwick will describe the Ontario Tumour Bank (OTB)’s repository of human cancer biospecimens and de-identified clinical data. Following this, Dr. Philip Awadalla will provide an overview of the Ontario Health Study (OHS) and describe research from his lab using simulation-based and machine learning tools to analyze evolution in non-recombining systems and the use of liquid biopsies to identify genetic and epigenetic markers indicative of cancer development. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and the availability of data and biosamples from OTB and the OHS.

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.