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Found 18 papers, 3 papers with code
COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity.
A Trustworthy Framework for Medical Image Analysis with Deep Learning
Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy.
Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model
Early identification of emergent topics is of eminent importance due to their potential impacts on society.
On the evolution of research in hypersonics: application of natural language processing and machine learning
Research and development in hypersonics have progressed significantly in recent years, with various military and commercial applications being demonstrated increasingly.
Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography
In this study, we propose a feature learning approach using Vision Transformers, which use an attention-based mechanism, and examine the representation learning capability of Transformers as a new backbone architecture for medical imaging.
Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem
Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources.
COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos
Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety.
Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis
In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required.
Improving Classification Model Performance on Chest X-Rays through Lung Segmentation
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications
Ablation study is conducted for both the performance and the trust on feature learning methods and loss functions.
NRC-GAMMA: Introducing a Novel Large Gas Meter Image Dataset
Motivated by the recent advances in the field of artificial intelligence and inspired by open-source open-access initiatives in the research community, we introduce a novel large benchmark dataset of real-life gas meter images, named the NRC-GAMMA dataset.
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
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, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus.
Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology
We hypothesized that knowledge production in Canadian nanotechnology is influenced by three key proximity factors, namely cognitive, geographical, and collaborative.
COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach
The COVID-19 pandemic has had devastating effects on the well-being of the global population.
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population.
Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing
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 lives and communities around the world in many ways, from cities under lockdown to new social experiences.
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics
We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery.
