About: Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening
Creator	Duddalwar, Vinay Gaikwad, Ashrika Gupte, Tanveer Kasliwal, Swaraj Kharat, Amit Kondal, Sudeep Kulkarni, Viraj Lokwani, Rohit Naik, Gunjan Pant, Aniruddha Saoji, Krishna
Source	ArXiv
abstract	Edge devices are revolutionizing diagnostics. Edge devices can reside within or adjacent to imaging tools such as digital Xray, CT, MRI, or ultrasound equipment. These devices are either CPUs or GPUs with advanced processing deep and machine learning (artificial intelligence) algorithms that assist in classification and triage solutions to flag studies as either normal or abnormal, TB or healthy (in case of TB screening), suspected COVID-19/other pneumonia or unremarkable (in hospital or hotspot settings). These can be deployed as screening point-of-care (PoC) solutions; this is particularly true for digital and portable X-ray devices. Edge device learning can also be used for mammography and CT studies where it can identify microcalcification and stroke, respectively. These solutions can be considered the first line of pre-screening before the imaging specialist actually reviews scans and makes a final diagnosis. The key advantage of these tools is that they are instant, can be deployed remotely where experts are not available to perform pre-screening before the experts actually review, and are not limited by internet bandwidth as the nano learning data centers are placed next to the device.
has issue date	2020-06-18(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f30f5e8949fddb5e25cae46b585d42e0d5aab726
resource representing a document's title	Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening
resource representing a document's body	covid:f30f5e8949fddb5e25cae46b585d42e0d5aab726#body_text
is schema:about of	named entity 'ultrasound' named entity 'These' named entity 'Role' named entity 'Device' named entity 'ADVANTAGE' named entity 'CENTERS' named entity 'USED' named entity 'IDENTIFY' named entity 'LEARNING' named entity 'BANDWIDTH' named entity 'IMAGING' named entity 'CLASSIFICATION' named entity 'THESE' named entity 'LIMITED BY' named entity 'SPECIALIST' named entity 'REVIEWS' named entity 'STUDIES' named entity 'TRUE' named entity 'EDGE' named entity 'SETTINGS' named entity 'UNREMARKABLE' named entity 'MRI' named entity 'solutions' named entity 'suspected' named entity 'CPUs' named entity 'ultrasound' named entity 'portable X-ray' named entity 'workflow' named entity 'infection' named entity 'machine learning' named entity 'cloud services' named entity 'Raspberry Pi' named entity 'digital imaging' named entity 'workflow' named entity 'follow-up' named entity 'molecular diagnostics' named entity 'edge device' named entity 'workflow' named entity 'Edge device' named entity 'cognitive process' named entity 'lost to follow-up' named entity 'CPU' named entity 'single-phase' named entity 'triage' named entity 'DICOM' named entity 'community-acquired pneumonia' named entity 'algorithm' named entity 'radiology' named entity 'radiology' named entity 'medical image' named entity 'nucleic acid' named entity 'World Health Organization' named entity 'latency' named entity 'encrypted' named entity 'ICU' named entity 'parallel computing' named entity 'teleradiology' named entity 'computing power' named entity 'narrow AI' named entity 'computing' named entity 'computing' named entity 'processing of data' named entity 'deep learning algorithms' named entity 'human-in-the-loop' named entity 'telecommunication' named entity 'PACS' named entity 'Tuberculosis' named entity 'healthcare workers' named entity 'NVIDIA' named entity 'data centers' named entity 'hardware components' named entity 'workflow' named entity 'computing' named entity 'chest X-ray' named entity 'radiologists' named entity 'edge computing' named entity 'end-to-end'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software