Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks
Author:
Affiliation:
1. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada
2. Deep Breathe, London, Canada
3. Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
Funder
Natural Sciences and Engineering Research Council of Canada (NSERC) through Vanier Scholar
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10332196.pdf?arnumber=10332196
Reference39 articles.
1. Comparative Diagnostic Performances of Auscultation, Chest Radiography, and Lung Ultrasonography in Acute Respiratory Distress Syndrome
2. Ultrasound Detection of Pneumothorax Compared with Chest X-Ray and Computed Tomography Scan
3. Lung ultrasound in critically ill patients: comparison with bedside chest radiography
4. Test Characteristics of Ultrasonography for the Detection of Pneumothorax
5. Accuracy of lung ultrasound for the diagnosis of consolidations when compared to chest computed tomography
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1. Intra-video positive pairs in self-supervised learning for ultrasound;Frontiers in Imaging;2024-06-20
2. Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification;Diagnostics;2024-05-22
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