Heart Disease

Thornhill, Rebecca

 250 250 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: rthornhill@toh.ca
Role: PhD faculty
Affiliation: Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa
Website: https://med.uottawa.ca/radiology/people/thornhill-rebecca
Data to Share: Not directly at present, but welcome opportunity to facilitate discussions with my clinical collaborators
Area(s) for Potential Collaboration: cardiac MRI and 1H/31P MRS of cardiovascular and metabolic disorders; brain/heart and liver/heart interactions; developing approaches for evaluating patient and clinician satisfaction with explanations for ML model decisions in medical imaging in cancer and heart disease
Modality: MR, CT
Organ: Cardiac, Brain, Kidney & Liver
Disease: Heart disease, Stroke and Cardiovascular, Cancer
Area of Research: Magnetic resonance imaging and spectroscopy of cardiovascular and metabolic disorders; radiomics and machine learning in medical imaging, including neuroimaging, oncologic, and cardiovascular applications; developing approaches to explainable ML in medical imaging.

Chen, Elvis

 400 400 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: chene@robarts.ca
Role: PhD faculty
Affiliation: Robarts Research Institute/Western University
Website: https://www.robarts.ca/research/scientists/elvis_chen.html
Data to Share: ultrasound images
Area(s) for Potential Collaboration: Atherosclerosis, cancer, orthopaedics
Modality: Ultrasound
Organ: Liver, Cardiovascular
Disease: Heart disease, Stroke and Cardiovascular, Cancer, Liver disease
Area of Research: ultrasound-guided surgical intervention.

McIntosh, Chris

 400 400 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: chris.mcintosh@uhn.ca
Role: PhD faculty
Affiliation: University Health Network, and University of Toronto
Website: https://mcintoshml.github.io/
Data to Share: Contact for information
Area(s) for Potential Collaboration: Model development and cross-centre validation
Modality: Ultrasound, MR, CT, X-ray
Organ: Prostate, Breast, Cardiac, Brain, Lung, Head and Neck
Disease: Heart disease, Stroke and Cardiovascular, Respiratory disease, Cancer
Area of Research: Machine Learning, computer vision, medical image segmentation, radiomics, radiation therapy treatment planning, wearables

Baldauf-Lenschen, Felix

 909 1024 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: felix@altislabs.com
Role: Industry
Affiliation: Altis Labs, Inc.
Website: https://www.altislabs.com/
Data to Share: >65,000 longitudinal CTs of NSCLC patients with associated clinical information (i.e. diagnostic, treatment history, demographics, outcomes). >33,000 chest x-rays of pneumonia and COVID-19 patients with associated clinical information (demographics, diagnostics, comorbidities, outcomes).
Area(s) for Potential Collaboration: 1) Prognostic imaging biomarker development (solid tumors, cardiology, respiratory diseases, neurology)
2) Annotation software for AI development
Modality: CT, X-ray
Organ: Lung
Disease: Heart disease, Respiratory disease, Cancer, Neurological disease, Liver disease, Kidney disease
Area of Research: Prognostic imaging biomarkers, labeling software development.

Narinder Paul

Paul, Narinder

 560 560 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: narinder.paul@lhsc.on.ca
Role: MD faculty
Affiliation: Western University
Website: https://www.schulich.uwo.ca/medimaging/about_us/chairs_message.html
Modality: MR, CT, X-ray
Organ: Cardiac, Lung
Disease: Heart disease, Respiratory disease, Cancer
Area of Research: Cardiothoracic, Lung Cancer

Ali T

Tavallaei, Ali

 1024 1024 MaLMIC - Machine Learning in Medical Imaging Consortium

Email: ali.tavallaei@ryerson.ca
Role: PhD faculty
Affiliation: Ryerson University
Website: https://www.ryerson.ca/electrical-computer-biomedical/people/faculty/ali-tavallaei/
Data to Share: Ultrasound
Modality: Ultrasound, MR
Organ: Cardiac, Vascular
Disease: Heart disease, Stroke and Cardiovascular
Area of Research: Systems and devices for minimally invasive cardiovascular interventions