Search Results for author: Maya Pavlova

Found 10 papers, 3 papers with code

Adversarial Text Normalization

no code implementations NAACL (ACL) 2022 Joanna Bitton, Maya Pavlova, Ivan Evtimov

Additionally, the process to retrain a model is time and resource intensive, creating a need for a lightweight, reusable defense.

Adversarial Text Natural Language Inference

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

no code implementations8 Jun 2022 Maya Pavlova, Tia Tuinstra, Hossein Aboutalebi, Andy Zhao, Hayden Gunraj, Alexander Wong

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally.

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

1 code implementation24 Apr 2022 Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee, Adrian Florea, Andrew Hryniowski, Alexander Wong

In this study we propose COVID-Net Biochem, an explainability-driven framework for building machine learning models to predict patient survival and the chance of developing kidney injury during hospitalization from clinical and biochemistry data in a transparent and systematic manner.

Injury Prediction

COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow

no code implementations14 Sep 2021 Audrey G. Chung, Maya Pavlova, Hayden Gunraj, Naomi Terhljan, Alexander MacLean, Hossein Aboutalebi, Siddharth Surana, Andy Zhao, Saad Abbasi, Alexander Wong

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 clinical workflow.

BIG-bench Machine Learning

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

1 code implementation5 Aug 2021 Alexander MacLean, Saad Abbasi, Ashkan Ebadi, Andy Zhao, Maya Pavlova, Hayden Gunraj, Pengcheng Xi, Sonny Kohli, Alexander Wong

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.

COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images

no code implementations14 May 2021 Maya Pavlova, Naomi Terhljan, Audrey G. Chung, Andy Zhao, Siddharth Surana, Hossein Aboutalebi, Hayden Gunraj, Ali Sabri, Amer Alaref, Alexander Wong

As the COVID-19 pandemic continues to devastate globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint.

Decision Making

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

no code implementations1 May 2021 Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee, Ali Sabri, Amer Alaref, Alexander Wong

More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16, 000 CXR images from a multinational cohort of over 15, 000 patient cases into a custom network architecture for severity assessment.

Transfer Learning

CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of Skin Cancer from Dermoscopy Images

1 code implementation21 Nov 2020 James Ren Hou Lee, Maya Pavlova, Mahmoud Famouri, Alexander Wong

Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public as part of the Cancer-Net initiative.


TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices

no code implementations10 Aug 2020 Alexander Wong, Mahmoud Famouri, Maya Pavlova, Siddharth Surana

In this study, we introduce the concept of attention condensers for building low-footprint, highly-efficient deep neural networks for on-device speech recognition on the edge.

speech-recognition Speech Recognition

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