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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
TLDR
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
TLDR
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest radiography images that is open source and available to the general public and investigated how it makes predictions using an explainability method.
COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images
TLDR
COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach, is introduced and the model and dataset are introduced.
Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
TLDR
The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.
Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing
TLDR
The results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario the authors wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning.
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
TLDR
This study introduces DepthNet Nano, a highly compact self normalizing network for monocular depth estimation designed using a human machine collaborative design strategy, where principled network design prototyping based on encoder-decoder design principles are coupled with machine-driven design exploration.
EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-Time Facial Expression Recognition
TLDR
This study proposes EmotionNet Nano, an efficient deep convolutional neural network created through a human-machine collaborative design strategy, where human experience is combined with machine meticulousness and speed in order to craft a deep neural network design catered toward real-time embedded usage.
Implications of Computer Vision Driven Assistive Technologies Towards Individuals with Visual Impairment
TLDR
The positive and negative implications computer vision based assistive technologies have on individuals with visual impairment are addressed, as well as considerations for computer vision researchers and developers in order to mitigate the amount of negative implications.
OLIV: An Artificial Intelligence-Powered Assistant for Object Localization for Impaired Vision
TLDR
Initial results from a proof-of-concept system trained to localize four different types of objects show promise to the feasibility of OLIV as a useful aid for individuals with impaired vision.
Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles
TLDR
This work proposes a new end to end architecture, Generalized Sensor Fusion (GSF), which is designed in such a way that both sensor inputs and target tasks are modular and modifiable, which paves the way for the industry to jointly design hardware and software architectures as well as large fleets with heterogeneous configurations.
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