DIY AI, Deep Learning Network Development for Automated Image Classification in a Point‐of‐care Ultrasound Quality Assurance Program

Journal of the American College of Emergency Physicians Open
doi 10.1002/emp2.12018
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Wiley

Related search

Automated Deep Learning Design for Medical Image Classification by Health-Care Professionals With No Coding Experience: A Feasibility Study

Lancet Digital Health, The
Health Information ManagementMedicineDecision SciencesHealth Informatics
2019English

TomoMQA: Automated Analysis Program for MVCT Quality Assurance of Helical Tomotherapy

Journal of Applied Clinical Medical Physics
InstrumentationNuclear MedicineRadiologyRadiationImagingMedicine
2020English

Automated Classification of Dense Calcium Tissues in Gray-Scale Intravascular Ultrasound Images Using a Deep Belief Network

BMC Medical Imaging
Nuclear MedicineRadiologyImaging
2019English

Online Social Network Image Classification and Application Based on Deep Learning

DEStech Transactions on Engineering and Technology Research
2017English

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

Remote Sensing
EarthPlanetary Sciences
2018English

A Deep Learning-Based Prediction Model for Gamma Evaluation in Patient-Specific Quality Assurance

Medical Physics
MedicineNuclear MedicineRadiologyImagingBiophysics
2018English

Quality Assurance for HIV Point-Of-Care Testing and Treatment Monitoring Assays

African Journal of Laboratory Medicine
EnvironmentalPublic HealthMedical Laboratory TechnologyClinical BiochemistryOccupational Health
2016English

Quality Assurance in Learning Material Development at OUM

Asian Association of Open Universities Journal
2017English

Quality Assurance Program

Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics
1959English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy