This code is for the paper COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning
Link to the paper
COVID-Net US-X is core part of COVID-Net, a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Link to COVID-Net portal: here
Recording to webinar on How we built COVID-Net in 7 days with Gensynth
Note: The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available. They are currently at a research stage and not yet intended as production-ready models (not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use COVID-Net for self-diagnosis and seek help from your local health authorities.
Pretrained models can be downloaded here: https://drive.google.com/drive/folders/11K6jntAc-72Uq5IMfpFHExO0naqvxub2?usp=sharing. Note that this pretrained COVID-Net US-X v2 model possesses a new machine-designed architecture based on the latest COVIDx US benchmark dataset, which is substantially larger and more diverse than that used in the study. As such, the final performance results here differ from that presented in the study
As the global population continues to face significant negative impact by the on-going COVID19 pandemic, there has been an increasing usage of point-of-care ultrasound (POCUS) imaging as a low-cost and effective imaging modality of choice in the COVID-19 clinical workflow. A major barrier with widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians that can interpret POCUS examinations, leading to considerable interest in deep learning-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we explore the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. Experimental results using an efficient deep columnar anti-aliased convolutional neural network designed via a machined-driven design exploration strategy (which we name COVID-Net US-X) show that the proposed extended linearconvex ultrasound augmentation learning significantly increases performance, with a gain of 5.1% in test accuracy and 13.6% in AUC.
An example of ultrasound images with different viewing windows. The bounds of the viewing windows are marked in red.
To generate a random transformation:
- Using the bounds of an ultrasound image defined by the corner points {p1left, p2left, p1right, p2right}, find a new slope based on the distribution N(old slope, σ).
- Define new points using the new slope.
- Estimate a transformation matrix based on the corner points
| Original | Transformed |
|---|---|
 (a) |
 (b) |
 (c) |
 (d) |
Projective data augmentation on convex ultrasound images (b) and linear ultrasound images (d)
If there are any technical questions after the README, FAQ, and past/current issues have been read, please post an issue or contact:
@article{zeng2022covid,
title={COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning},
author={Zeng, E Zhixuan and Florea, Adrian and Wong, Alexander},
journal={arXiv preprint arXiv:2204.13851},
year={2022}
}
Required python libraries can be found in requirements.txt
Install COVID-US data by following instructions here
Train/valid/test split is shown in COVID-Net-US-projective-augs/labels
Make sure that eval_hyperparameters.json is pointing to the desired model path, then run python eval.py. The results would be saved to the same directory. Model paths can also use pattern matching accepted by glob such as "runs/*/model.pth"
There are some jupyter notebooks in the notebooks folder that might be useful for demos, testing, etc.
notebooks/image_warping.ipynb is useful for testing projective and piecewise affine warping found in random_warp.py. notebooks/test_dataloader.ipynb runs the train dataloader given settings in hyperparameters.json to view the resulting images. image_warping.ipynb allows for looking at specific images and setting specific degrees of warping while test_dataloader.ipynb shows randomized results.