MaLMIC - Machine Learning in Medical Imaging Consortium
MaLMIC is pleased to support three sessions at the ImNO 2024 symposium in Toronto ON in March 2024.
The October forum will focus on using machine learning to predict disease progression. Our first speaker will be Xia Jiang who will give an overview of the application of machine learning to electronic health record data for the prognostication of clinical outcomes. Our second speaker, 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 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. Our first speaker will be Xia Jiang who will give an overview of the application of machine learning to electronic health record data for the prognostication of clinical outcomes. Our second speaker, 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 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 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.
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.
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.
The Canadian Institute for Advanced Research (CIFAR), the University of Alberta (UAlberta), and the Alberta Machine Intelligence Institute (Amii) are pleased to announce $30M in funding to recruit 20 new AI Research Chairs at UAlberta.
The Canadian Institute for Advanced Research (CIFAR), the University of Alberta (UAlberta), and the Alberta Machine Intelligence Institute (Amii) are pleased to announce $30M in funding to recruit 20 new AI Research Chairs at UAlberta.
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