Past Events

Join us for our upcoming forum featuring two exciting talks exploring Machine Learning to Improve Surgical Outcomes.

Listen in to two exciting talks from the Virtual Health Hub, which explored how artificial intelligence and virtual care are transforming access to healthcare.
Dr. Scott Adams examined how AI-assisted imaging is redefining medical imaging and expanding access to advanced diagnostics in underserved communities. He also showcased current projects demonstrating how AI can support a more connected, equitable, and sustainable health system.
Dr. Ivar Mendez focused on how virtual care, when grounded in local partnerships and cultural safety, can improve health outcomes in rural, remote, and Indigenous communities. He highlighted the AI tools developed to enable scalable virtual care solutions across Canada and beyond.

Listen to this high-impact session on AI for brain imaging that bridged cellular detail and clinical translation. Ahmadreza Attarpour’s talk showcased ACE (Artificial intelligence-based Cartography of Ensembles) and MAPL3 (Mapping Axonal Projections in Light-sheet fluorescence microscopy in 3D): end-to-end deep-learning pipelines that turn teravoxel, cellular-resolution light-sheet datasets from cleared rodent brains into unbiased, brain-wide maps of local neuronal activity and connectivity—at both cellular and laminar scales. The second talk by Mahmoud Salman shifted to the neonatal clinic, introducing a robust deep-learning framework that overcomes poor contrast, motion artifacts, and tiny target structures to precisely segment hippocampal subfields and amygdala sub-nuclei from routine MRI. Together, these talks demonstrated how scalable, generalizable methods are pushing neuroscience forward—from whole-brain mapping at unprecedented scale to reliable quantitative biomarkers of early brain maturation.

Drs. Amoon Jamzad and Laura Connolly shared two cutting-edge perspectives on advancing cancer surgery through robotics and intelligent tools. Laura spoke about her work on integrating AI-enabled robotics and haptic feedback into breast-conserving surgery to improve tumor removal in highly mobile, deformable tissue. She also shared how robotics combined with photoacoustic imaging can inspect resection cavities post-surgery, aiming for a future where no cancer is left behind. Amoon presented his research on applying deep learning to mass spectrometry data—capturing molecular tissue signatures in real time—to help surgeons accurately distinguish healthy from cancerous tissue. Amoon highlighted strategies for making these models trustworthy, explainable, and generalizable, and for translating mass spectrometry into intraoperative and post-resection workflows. Together, these innovations promise to enhance surgical precision, reduce positive margins, and improve patient outcomes.

Glenn Bauman and Katherine Zukotynski spoke 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 presented at the MaLMIC fall forum, hosted by Julia Publicover. The presentations were 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 presented at the MaLMIC fall forum, hosted by Amber Simpson. The presentations were 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 focused on computational pathology for outcome prediction in cancer. Mattias Rantalainen was the first speaker talking about his research program and the work of Stratipath predictive models that are being translated into the clinic. Faisal Mahmood, the second speaker, spoke 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 were followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

This forum focused on translating AI-based innovations in medicine into the commercial world and the clinic. The first speaker, Qian Cao, spoke 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, spoke about the complexities of translating AI/ML ideas from a manufacturer’s perspective and showcase examples. The presentations were followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

The October forum focused on using machine learning to predict disease progression. Su-In Lee, described the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation was 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, focused on human sample and data resources for machine learning research. Dr. Dianne Chadwick described the Ontario Tumour Bank (OTB)’s repository of human cancer biospecimens and de-identified clinical data. Following this, Dr. Philip Awadalla provided an overview of the Ontario Health Study (OHS) and described 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 were 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 described 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, was about the past, present and future of digital pathology and AI. The second presentation by Phedias Diamandis, a pathologist, was about approaches to solve implementation challenges of AI in the real world. The talks were 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, was about AI and Precision Health. The second presentation by Jacob Jaremko a radiologist, entrepreneur and AI scientist, was about AI augmented ultrasound imaging. The talks were 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, was about CT image reconstruction and the second presentation by Charlie Millard, a postdoctoral fellow, was about MRI image reconstruction. The talks were followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

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

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

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