Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data

[Display omitted] •Presented a systematic approach to learn from imbalanced set of bio-medical images.•Developed a practical “survival of the fittest” approach for hyperparameter tuning of models.•Proposed a framework to use leftout imbalanced data for pseudo-testing purpose.•Provided a publicly ava...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Methods (San Diego, Calif.) Jg. 202; S. 31 - 39
Hauptverfasser:	Chan, Jonathan H., Li, Chenqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.06.2022
Schlagworte:	case studies chest Chest X-ray COVID-19 COVID-19 - diagnostic imaging COVID-19 infection data collection Deep Learning Deep neural networks health care workers Humans image analysis Imbalanced data Medical imaging Pandemics SARS-CoV-2 Tomography, X-Ray Computed - methods Transfer learning X-radiation X-Rays COVID-19 Imbalanced data Medical imaging Deep neural networks Chest X-ray Transfer learning
ISSN:	1046-2023, 1095-9130, 1095-9130
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!