15 citations · 40 across the 20 of their papers we have counts for
13 papers · 1 filter
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
Siyuan He, Pengcheng Xi, Ashkan Ebadi +2
Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between pe…
MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis
Hossein Aboutalebi, Maya Pavlova, Hayden Gunraj +4
Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In…
COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
Audrey G. Chung, Maya Pavlova, Hayden Gunraj +7
As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 cli…
COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification
Audrey Chung, Mahmoud Famouri, Andrew Hryniowski +1
The COVID-19 pandemic continues to have a devastating global impact, and has placed a tremendous burden on struggling healthcare systems around the world. Given the limited resourc…
COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging
Alexander MacLean, Saad Abbasi, Ashkan Ebadi +6
The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections…
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution
George Michalopoulos, Ian McKillop, Alexander Wong +1
Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results…