POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound
This addresses the need for rapid mass testing in rural and third-world settings, potentially reducing reliance on trained professionals and scarce testing kits.
The paper tackled the problem of inefficient COVID-19 testing by automating detection using point-of-care ultrasound, achieving significant improvements in state-of-the-art detection accuracies with a real-time capable architecture.
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 environments and third world countries. Our contributions towards rapid large-scale testing include 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.