– MR

Sensitivity of convolutional neural networks to common imaging parameters, perturbations and artifacts in MRI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

lyndon_malmic_2021 Open Forum on Alzheimer’s and Small Vessel Disease Imaging Past Event Page Presenter: Lyndon Boone Abstract: A number of studies have shown that deep learning methods are capable of…

Deep Learning for Automated Segmentation of Left Ventricle Myocardium and Myocardial Scar From 3-D MR Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

2021-06-18Zabihollahy Open Forum on Machine Learning in Cardiac Imaging Past Event Page Presenter: Fatemeh Zabihollahy Abstract: Deep learning has demonstrated promise for various cardiac imaging applications. However, the performance is…

Deep learning with uncertainty quantification in MRI-guided radiation therapy

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

2021-05-21Hemsley Open Forum on Reproducibility and Good Practices in Machine Learning Research Past Event Page Presenter: Matt Hemsley Abstract: Deep learning methods are able to match or surpass the performance…

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Brain-CODE2021-03-19 Open Forum on Machine Learning in Health Data Integration Platforms Past Event Page Presenters: Tom Mikkelsen, Kirk Nylen, Brendan Behan Abstract: The Ontario Brain Institute (OBI) provided an overview…

MRI in Prostate Cancer: Opportunities for AI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

OICR-Prostate-ML-Haider Open Forum on Machine Learning in Prostate Cancer Past Event Page Presenter: Masoom Haider Abstract: MRI has established itself as an adjunctive test in detecting occult prostate cancer. New…

Radiomics and Machine Learning for Oropharyngeal Cancer

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Mattonen2021-01-21 Open Forum on Machine Learning in Head and Neck Cancer Past Event Page Presenter: Sarah Mattonen Abstract: Sarah discussed preliminary work investigating radiomics and machine learning models to predict…