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Found 13 papers, 1 papers with code
Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology
Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life.
COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
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.
COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images
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.
COVID-Net S: Towards computer-aided severity assessment via 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
Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0. 664 $\pm$ 0. 032 and 0. 635 $\pm$ 0. 044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments.
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care
We propose a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate's remaining food volume relative to reference portions in whole and modified texture foods.
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms.
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devices.
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations.
A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared pureed food concentrations
This DNN imaging system for nutrient density analysis of pureed food shows promise as a novel tool for nutrient quality assurance.
Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection
We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically-proven diagnostic data from the LIDC-IDRI dataset.
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data.
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
In this study, we propose a novel \textit{discovery radiomics} framework for generating custom radiomic sequences tailored for prostate cancer detection.
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data.
