Abstract A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause disease 2019 (COVID-19) pandemic, is assessment of severity progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important metrics being extent lung involvement degree opacity. In this proof-of-concept study, we feasibility computer-aided scoring CXRs using a deep learning system. Data consisted 396 from positive...

As the COVID-19 pandemic devastates globally, use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues grow given its routine clinical for respiratory complaint. part COVID-Net open source initiative, we introduce CXR-2, an enhanced deep convolutional neural network design detection from CXR images built using greater quantity and diversity patients than original COVID-Net. We also new benchmark dataset composed 19,203 multinational cohort 16,656 at...

Quantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization prostate cancer. While most cancer algorithms utilize either voxel-based or region-based feature models, the complexity tumour phenotype in MP-MRI requires more sophisticated framework to better leverage available exploit priori knowledge field. In this paper, we present MPCaD, novel Multi-scale...

Abstract Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake LTC laborious and subjective, limiting clinical inference capabilities. Recent advances automatic image-based food estimation have not yet been evaluated settings. Here, we describe fully imaging system for quantifying intake. We propose novel deep convolutional encoder-decoder network with depth-refinement (EDFN-D) using an RGB-D camera plate’s remaining volume...

Is creativity domain-specific

The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection prostate tumors, since it provides a plethora mineable data, which can be used both detection and prognosis cancer. While current voxel-resolution radiomics-driven tumor approaches utilize associated with individual voxels on an independent basis, incorporation additional information regarding spatial feature relationships...

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. Recently, there have been greater efforts the design of small, low-footprint neural networks (DNNs) that are more appropriate devices, with much focus on principles hand-crafting efficient network architectures. In this study, we explore a human-machine collaborative strategy building DNN architectures through...

New Zealand's political, civic, health and social institutions have been criticised as being ill-prepared to serve the needs of country's increasingly diverse ageing population. This grounded theory study examined how late-life Asian immigrants participate in community influence their subjective health. Bilingual Chinese, Indian, Korean local intermediaries research assistants were engaged collaborative partners. Purposive recruitment, later theoretical sampling, used identify 24 27 25...

A novel method, Stochastically Acquired Photoplethysmo-gram for Heart rate Inference in Realistic Environments (SAPPHIRE), is proposed robust remote heart measurement through broadband video. set of stochastically sampled points from the cheek region tracked and used to construct corresponding time series observations via skin erythema transforms. From these observations, a photo-plethysmogram (PPG) waveform estimated Bayesian minimization, with required posterior probability inferred using...

Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number studies that GAN-based image synthesis augmentation can aid in improving classification accuracy medical tasks, such as brain and liver analysis. However, efficacy leveraging GANs tackling prostate cancer has not been previously explored. Motivated by this, this study we introduce ProstateGAN,...

Radiomics has proven to be a powerful prognostic tool for cancer detection, and previously been applied in lung, breast, prostate, head-and-neck studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability characterize unique traits. In this study, we introduce novel discovery radiomics framework where directly discover custom features from the wealth of available medical imaging data. particular,...

While lung cancer is the second most diagnosed form of in men and women, a sufficiently early diagnosis can be pivotal patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate differences between cancerous healthy tissue. Recently, concept discovery radiomics was introduced, where custom abstract are discovered from readily available imaging data. We...

Prostate cancer is the most diagnosed form of in Canadian men, and third leading cause death. Despite these statistics, prognosis relatively good with a sufficiently early diagnosis, making fast reliable prostate detection crucial. As imaging-based screening, such as magnetic resonance imaging (MRI), requires an experienced medical professional to extensively review data perform radiomics-driven methods help streamline process has potential significantly improve diagnostic accuracy...

We present a novel non-contact photoplethysmographic (PPG) imaging system based on high-resolution video recordings of ambient reflectance human bodies that compensates for body motion and takes advantage skin erythema fluctuations to improve measurement reliability the purpose remote heart rate monitoring. A single location recording is automatically identified an individual, determined over time via tracking. Based information motion-compensated measurements at different wavelengths can be...

We propose an effective framework for salient region detection in natural images based on the concept of self-guided statistical non-redundancy (SGNR). Salient regions are unique, because they have low information redundancy within a given image, while rest scene may highly be redundant. first analyze structural characteristics image using structured elements (samples) and classify them as being non-redundant or redundant textural compactness overall non-redundancy. This guides saliency...

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19 pandemic, is assessment severity disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important metrics being extent lung involvement degree opacity. In this proof-of-concept study, we feasibility computer-aided scoring CXRs using a deep learning system. Materials Methods: Data consisted 396...

Lung cancer is the second most common in United States, regardless of gender. staging a critical process for diagnosis and prognosis that commonly done through analysis computed tomography chest. Analysis can be by extracting quantitative metrics from clinician defined contours; however, defining contours manually time consuming which, an environment where fast necessary, undesirable. Semi-automatic methods are desirable minimizing user input while achieving similar contouring results....

<p>In this paper, we describe the underlying methodology behind discovery<br />radiomics, where ultimate goal is to discover customized,<br />abstract radiomic feature models directly from wealth of medical<br />imaging data better capture highly unique tumor traits beyond<br />what can be captured using hand-crafted feature<br />models. We further explore current state-of-the-art in />radiomics and their application various forms cancer such<br...