Corpus ID: 237532338

The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays

@article{Harkness2021ThePO,
  title={The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays},
  author={R. Angus Harkness and Geoff Hall and Alejandro F. Frangi and Nishant Ravikumar and Kieran Zucker},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.08020}
}
Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used… Expand

Figures and Tables from this paper

References

SHOWING 1-6 OF 6 REFERENCES
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
TLDR
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases. Expand
CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images
TLDR
A novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease and develops explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. Expand
Automated detection of COVID-19 cases using deep neural networks with X-ray images
TLDR
A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients. Expand
The RSNA International COVID-19 Open Radiology Database (RICORD)
TLDR
The RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD), the first multi-institutional, multinational, expert-annotated CO VID-19 imaging data set, which is made freely available to the machine learning community. Expand
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TLDR
A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset. Expand
COVID-19 Image Data Collection, ArXiv:2003.11597 [Cs, Eess, q-Bio
  • http://arxiv.org/abs/2003.11597 (accessed April
  • 2020