@inproceedings{95e8d3776d1e4759b6b3ceb2a1e8cd47,
title = "Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System",
abstract = "This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system. The adaptive visual diagnostics utilize few-shot learning (FSL) to generate encoded disease state models that are stored and classified using a dictionary of knowns. The novel vocabulary based feature processing of the pipeline adapts the knowledge of a pretrained deep neural network to compress the ultrasound images into discrimative descriptions. The computational efficiency of the FSL approach enables high diagnostic deep learning performance in PoC settings, where training data is limited and the annotation process is not strictly controlled. The algorithm performance is evaluated on the open source COVID-19 POCUS Dataset to validate the system's ability to distinguish COVID-19, pneumonia, and healthy disease states. The results of the empirical analyses demonstrate the appropriate efficiency and accuracy for scalable PoC use. The code for this work will be made publicly available on GitHub upon acceptance.",
keywords = "COVID-19, Deep Neural Networks, Few-Shot Learning, POC Ultrasound",
author = "Michael Karnes and Shehan Perera and Srikar Adhikari and Alper Yilmaz",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",
year = "2021",
doi = "10.1109/BioCAS49922.2021.9645029",
language = "English (US)",
series = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
}