Point of Care Image Analysis for COVID-19
Daniel Yaron, Daphna Keidar, Elisha Goldstein, Yair Shachar, Ayelet Blass, Oz Frank, Nir Schipper, Nogah Shabshin
Abstract
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to dis-infect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.
BibTeX
@inproceedings{icassp2021_pointofcareimage,
title = {Point of Care Image Analysis for COVID-19},
author = {Daniel Yaron and Daphna Keidar and Elisha Goldstein and Yair Shachar and Ayelet Blass and Oz Frank and Nir Schipper and Nogah Shabshin and Ahuva Grubstein and Dror Suhami and Naama R. Bogot and Chedva S. Weiss and Eyal Sela and Amiel A. Dror and Mordehay Vaturi and Federico Mento and Elena Torri and Riccardo Inchingolo and Andrea Smargiassi and Gino Soldati and Tiziano Perrone and Libertario Demi and Meirav Galun and Shai Bagon and Yishai M. Elyada and Yonina C. Eldar},
booktitle = {ICASSP 2021},
year = {2021}
}