Past Events

View past events, presentations/videos and workshops. You can also view previous presentations and video talks from past forum outcomes.

MaLMIC sponsored machine learning sessions at ImNO 2024 – Symposium Session March 2024

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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!

Computational pathology and cancer prediction – Forum, January 19th, 2024

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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.

Translating AI/ML into products – Forum, November 24, 2023

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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.

Machine Learning to Predict Disease Progression – Forum, October 20, 2023

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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.

Ontario Tumour Bank and Ontario Health Study: Machine learning resources and projects – Forum, September 29, 2023

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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.

Med-ImageNet: Creation of a Compendium of AI-Ready Medical Imaging Data for Deep Learning Analysis – Forum, July 21, 2023

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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.

Machine Learning in Computational Neuroimaging- Forum, June 23, 2023

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Enforcing geometry in machine learning for computational neuroimaging
The June forum centers on the vital role of geometric principles in machine learning applications for medical imaging. The first presentation by Ali Khan will address machine learning applications in computational neuroimaging, exploring how geometry can inform the representation of neuroanatomy. The second presentation by Uzair Hussain will explore geometric machine learning in computational diffusion MRI, describing a novel approach for performing machine learning on data modelled as a sphere. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Machine Learning in Computational Pathology- Forum, May 26, 2023

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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.

Machine Learning and Precision Health – Forum, April 21, 2023

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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.

MaLMIC sponsored machine learning session at ImNO 2023 – Symposium Session, March 23/24, 2023

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MaLMIC is pleased to support three sessions at the ImNO 2023 symposium in London ON on Friday, March 24

Machine Learning in Image Reconstruction – Forum, January 20, 2023

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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.

Machine Learning in SaRS-COV-2 and COVID Medical Imaging – Forum, November 18, 2022

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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.

Natural Language Processing – Forum, October 28, 2022

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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.

Machine Learning in Radiology – Forum, July 15 2022

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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.

Machine learning in Liquid Biopsy – Forum, June 17 2022

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Alex Wyatt will give a general introduction to liquid biopsy. This will be followed by Eric Zhao talking about his research work using liquid biopsy in head and neck cancer, and Nick Cheng talking about his research work using liquid biopsy for early detection of high-risk breast cancer. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Machine Learning in Vascular Imaging– Forum, May 27, 2022

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David Spence, a clinician, will talk about the need for machine learning applications in vascular imaging. His presentation will be followed by two researchers, Eran Ukwatta and Fumin Guo, talking about their work in vascular imaging machine learning. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Machine learning for molecular and cellular imaging – Forum, April 22, 2022

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Hartland Jackson will talk about machine learning applied to imaging mass cytometry. Parvin Mousavi will present her work on mass spectrometry applied to real-time intraoperative tumour margin detection. David Andrews will talk about machine learning in high throughput single-cell imaging. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

MaLMIC sponsored machine learning session at ImNO 2022 – Forum, March 23, 2022

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MaLMIC is organizing a machine learning session at ImNO 2022 on Wednesday, March 23, 2022. Alison Noble, a research scientist at University of Oxford, will give a keynote presentation followed by oral and pitch presentations focused on imaging machine learning applications.

Federated learning – Forum, February 18, 2022

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Kam Kafi, Director of Clinical Strategy and Oncology at Imagia will outline the case for federated learning and work underway between Imagia and collaborators. Spyridon Bakas, Assistant Professor at UPenn, will talk about an open platform he has developed to support federated learning for radiology. Aline Talhouk, Assistant Professor at UBC, will talk about privacy issues and introduce the LEAP platform. The presentations will be followed by group discussion on lessons learned, opportunities to collaborate, and sharing of resources.

Machine learning in image-guided intervention – Forum, January 21, 2022

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Derek Cool, an interventional radiologist, discussed the clinical need for machine learning in image-guided intervention. Purang Abolmaesumi and Derek Gillies then presented their research work in the area. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Breast Cancer Machine Learning – Forum, November 19, 2021

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Dr. Emily Conant, a radiologist from the University of Pennsylvania, gave the lead-off talk and presented the clinical problems and opportunities where machine learning could make significant impact. She was followed by Professor Nico Karssemeijer from Radboud University in the Netherlands. He has developed important algorithms for breast density and is implementing AI approaches to detection of breast cancer. Grey Kuling, PhD candidate from Sunnybrook Research Institute gave the third talk covering the more technical approach of their research work. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Lessons Learned in Genomics and Imaging Machine Learning – Forum, October 15, 2021

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Phedias Diamandis, a neuropathologist and researcher at UHN, discussed the clinical challenges of classifying brain malignancies and the promise of combining ‘omics with digital morphology to help untangle the substantial heterogeneity in these tumours. This was followed by a talk by Wail Ba-Alawi, an affiliate scientist at Princess Margaret Cancer Centre, on using multi-omics techniques for cancer biomarker discovery, and by Anglin Dent, an MSc student at University of Toronto, who expanded on Phedias’ talk by discussing progress in applying deep learning approaches to define biologically distinct subpopulations in glioblastoma. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Immunotherapy Imaging – Forum, September 17, 2021

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Annette Hay, a hematologist and clinician-scientist, talked about the ExCELLirate platform followed by two researchers, Tricia Cottrell and Alison Cheung, described their immunotherapy imaging machine learning research. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Alzheimer’s and Small Vessel Disease Imaging – Forum, July 16, 2021

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Sandra Black, a neurologist, talked about the clinical perspective followed by two researchers, Maged Goubran and Lyndon Boone, describing their machine learning research in the area. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Cardiac Machine Learning – Forum, June 18, 2021

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James White, a cardiologist, talked about the clinical perspective followed by two researchers, Fatemeh Zabihollahy and Fumin Guo, described their machine learning research in the area. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Reproducibility and good practices for machine learning research – Focused Meeting, May 26, 2021

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On May 21, MaLMIC held an open forum on reproducibility and best practices in machine learning research. Thank you to those that participated in the follow up in-depth meeting on Reproducibility and good practices for machine learning research.

Reproducibility and good practices for machine learning research – Forum, May 21, 2021

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Benjamin Haibe-Kains, Chaya Moskowitz and Matt Hemsley talked about reproducibility and good practices in machine learning research. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Pathology – Focused Meeting, April 21, 2021

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On April 16, MaLMIC held an open forum on machine learning in pathology. Thank you to those that participated in the follow up in-depth meeting on machine learning in pathology.

Pathology – Forum, April 16, 2021

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Dr. David Berman, a pathologist, discussed unmet clinical needs in pathology machine learning followed by two researchers, Dr. Ali Bashasati and Dr. Chetan Srinidhi, described their machine learning research work in the area. This was followed by discussion focused on opportunities to collaborate and share resources.

Health Data Integration Platforms – Focused Meeting, March 31, 2021

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On March 19, MaLMIC held an open forum on health data integration platforms. Thank you to those who participated on Wednesday, March 31, 2021 at 4 p.m., EDT. in a follow up in-depth meeting on machine learning and health data integration platforms.

Health Data Integration Platforms – Forum, March 19, 2021

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Representatives from Quantitative Imaging for Personalized Cancer Medicine, Brain-CODE and Ontario Health Data Platform presented overviews of their data integration platforms. This was followed by discussion focused on opportunities to collaborate and share resources.

Machine Learning and Prostate Cancer – Focused Meeting, Feb. 24, 2021

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On February 19, 2021 the Machine Learning in Medical Imaging Consortium held a forum focused on prostate cancer. Thank you to those who participated in a follow up in-depth meeting on prostate cancer machine learning and data sharing.

Prostate Cancer – Forum, February 19, 2021

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Dr. Haider, a radiologist, discussed the prostate cancer unmet clinical need followed by two researchers, Dr. Mousavi and Mr. Orlando, describing their machine learning research work in the area. This was followed by discussion focused on opportunities to collaborate and share resources.

Head and Neck Cancer – Forum, January 22, 2021

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Dr. Lang, a radiation oncologist, discussed the unmet clinical need followed by two researchers, Dr. Mattonen and Mr. Kazmierski, described their machine learning research work in the area. This was followed by discussion focused on opportunities to collaborate and share resources.

Radiomics and Machine Learning in Oncological Medical Imaging – Forum, October 28, 2020

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The presenters gave short talks highlighting their current research work, their unique approach, datasets they are using, advantages of their strategy, current collaborations and future opportunities.