Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images-Reference-Cited by-同舟云学术

Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images

Published:2021-12 Issue:6 Volume:150 Page:4118-4127
ISSN:0001-4966
Container-title:The Journal of the Acoustical Society of America
language:en
Short-container-title:The Journal of the Acoustical Society of America

Author:

Roshankhah Roshan¹^ORCID,Karbalaeisadegh Yasamin²,Greer Hastings³,Mento Federico⁴^ORCID,Soldati Gino⁵,Smargiassi Andrea⁶,Inchingolo Riccardo⁶,Torri Elena⁷,Perrone Tiziano⁸,Aylward Stephen³,Demi Libertario⁴^ORCID,Muller Marie¹

Affiliation:

1. Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27606, USA

2. Georgia Institute of Technology, Atlanta, Georgia 30332, USA

3. Kitware Inc., Clifton Park, New York 12065, USA

4. Ultrasound Laboratory, University of Trento, Trento, Italy

5. Azienda USL Toscana nord ovest Sede di Lucca, Diagnostic and Interventional Ultrasound Unit Lucca, Toscana, Italy

6. Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS. Roma, Lazio, Italy

7. BresciaMed, Brescia, Italy

8. Department of Internal Medicine, Istituto di Ricovero e Cura a Carattere Scientifico, San Matteo, Pavia, Italy

Funder

National Institutes of Health

H2020 European Institute of Innovation and Technology

VRT foundation

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Link

https://asa.scitation.org/doi/pdf/10.1121/10.0007272

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