WORK IN PROGRESS.
This repo is a fork of https://github.com/jannisborn/covid19_ultrasound which contains the code for the paper Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis.
This is an ongoing ultrasound data collection initiative for COVID-19. Please help growing the database.
Feel free to use (and cite) the paper dataset. They currently have >200 LUS videos. For details see https://github.com/jannisborn/covid19_ultrasound/data/README.md. Please note: The founders/authors of the repository take no responsibility or liability for the data contributed to this archive. The contributing sites have to ensure that the collection and use of the data fulfills all applicable legal and ethical requirements.
Work in progress
From the ML community, ultrasound has gained much less attention than CT and X-Ray in the context of COVID-19. But many voices from the medical community have advocated for a more prominent role of ultrasound in the current pandemic.
We developed methods for the automatic detection of COVID-19 from Lung Ultrasound (LUS) recordings. Our results show that one can accurately distinguish LUS samples from COVID-19 patients from healthy controls and bacterial pneumonia. Our model is validated on an external dataset (ICLUS) where we achieve promising performance. The CAMs of the model were validated in a blinded study by US experts and found to highlight relevant pulmonary biomarkers. Using model uncertainty techniques, we can further boost model performance and find samples which are likely to be incorrectly classified. Our dataset complements the current data collection initiaves that only focus on CT or X-Ray data.
From https://github.com/jannisborn/covid19_ultrasound
Ultrasound is non-invasive, cheap, portable (bedside execution), repeatable and available in almost all medical facilities. But even for trained doctors detecting patterns of COVID-19 from ultrasound data is challenging and time-consuming. Since their time is scarce, there is an urgent need to simplify, fasten & automatize the detection of COVID-19.
Find all details on the current state of the database in the data folder.
- If you experience problems with the code, please open an issue.
- If you have questions about the project, please reach out:
mathieu.febvay@eric.univ-lyon2.fr.
Please use the following bibtex entries: