@inproceedings{da756e7536c041a788467a378187c68a,
title = "POCFORMER: A LIGHTWEIGHT TRANSFORMER ARCHITECTURE FOR DETECTION OF COVID-19 USING POINT OF CARE ULTRASOUND",
abstract = "The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environment and third world countries. Our contributions towards rapid large-scale testing includes a novel deep learning architecture capable of analyzing ultrasound data that can run in real time and significantly improve the current state-of-the-art detection accuracies using image based COVID-19 detection.",
keywords = "Covid-19 diagnosis, Deep learning, Transformer networks, Ultrasound",
author = "Shehan Perera and Srikar Adhikari and Alper Yilmaz",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Image Processing, ICIP 2021 ; Conference date: 19-09-2021 Through 22-09-2021",
year = "2021",
doi = "10.1109/ICIP42928.2021.9506353",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "195--199",
booktitle = "2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings",
}