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20202023
most citedCOVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images

15 citations · 40 across the 20 of their papers we have counts for

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Showing cs.LGShow all

6 papers · 1 filter

cs.LG2022

COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation

Pengyuan Shi, Yuetong Wang, Saad Abbasi +1

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and rec…

cs.LG2022

COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data

Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee +3

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SA…

cs.LG2021★ 3 cited

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…

cs.LG2021

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…

cs.LG2021

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…

cs.LG2020

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing

Ashkan Ebadi, Pengcheng Xi, Stéphane Tremblay +3

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human live…