University Health Network

∙ Toronto , Canada ∙ 0 followers

The University Health Network (UHN), consisting of Princess Margaret Cancer Centre, Toronto General Hospital, Toronto Western Hospital and Toronto Rehabilitation Institute, is a recognized leader in patient care, research and education.

research

∙ 12/23/2019

Analysis of the hands in egocentric vision: A survey

Egocentric vision (a.k.a. first-person vision - FPV) applications have t...

Andrea Bandini, et al. ∙

research

∙ 08/09/2022

Inconsistencies in Measuring Student Engagement in Virtual Learning – A Critical Review

In recent years, virtual learning has emerged as an alternative to tradi...

Shehroz S. Khan, et al. ∙

research

∙ 10/12/2016

Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders

A fall is an abnormal activity that occurs rarely, so it is hard to coll...

Shehroz S. Khan, et al. ∙

research

∙ 02/08/2014

Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling

Ultra low radiation dose in X-ray Computed Tomography (CT) is an importa...

SayedMasoud Hashemi, et al. ∙

research

∙ 07/17/2018

Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks

Knowledge-based planning (KBP) is an automated approach to radiation the...

Rafid Mahmood, et al. ∙

research

∙ 08/30/2018

DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Human falls rarely occur; however, detecting falls is very important fro...

Jacob Nogas, et al. ∙

research

∙ 11/11/2018

A Survey of Mixed Data Clustering Algorithms

Most of the datasets normally contain either numeric or categorical feat...

Amir Ahmad, et al. ∙

research

∙ 12/03/2018

Prediction of New Onset Diabetes after Liver Transplant

25 within the next 5 years. These thousands of individuals are at 2-fold...

Angeline Yasodhara, et al. ∙

research

∙ 12/03/2018

Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies

Medical imaging machine learning algorithms are usually evaluated on a s...

Ahmed Ashraf, et al. ∙

research

∙ 01/31/2019

A Novel Initial Clusters Generation Method for K-means-based Clustering Algorithms for Mixed Datasets

Mixed datasets consist of numeric and categorical attributes. Various K-...

Amir Ahmad, et al. ∙

research

∙ 05/19/2019

Spatio-Temporal Adversarial Learning for Detecting Unseen Falls

Fall detection is an important problem from both the health and machine ...

Shehroz S. Khan, et al. ∙

research

∙ 10/06/2020

RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray

COVID-19 spread across the globe at an immense rate has left healthcare ...

Saman Motamed, et al. ∙

research

∙ 09/09/2021

Modified Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours

Detecting distracted driving behaviours is important to reduce millions ...

Shehroz S. Khan, et al. ∙

University Health Network

Featured Researchers

Shehroz S. Khan

Babak Taati

Ali Abedi

Andrea Bandini

Shehroz Khan

Narinder S. Paul

Andrea McNiven

Mamatha Bhat

Patrik Rogalla

Tracey Colella

Analysis of the hands in egocentric vision: A survey

Inconsistencies in Measuring Student Engagement in Virtual Learning – A Critical Review

Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders

Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling

Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks

DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

A Survey of Mixed Data Clustering Algorithms

Prediction of New Onset Diabetes after Liver Transplant

Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies

A Novel Initial Clusters Generation Method for K-means-based Clustering Algorithms for Mixed Datasets

Spatio-Temporal Adversarial Learning for Detecting Unseen Falls

RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray

Modified Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours