Events

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.