Past Events

Natural Language Processing – Forum, October 28, 2022

1024 545 MaLMIC - Machine Learning in Medical Imaging Consortium

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

1000 563 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

1024 767 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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.