Corpus ID: 219530597

End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays

@article{Signoroni2020EndtoendLF,
  title={End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays},
  author={A. Signoroni and Mattia Savardi and Sergio Benini and N. Adami and R. Leonardi and Paolo Gibellini and F. Vaccher and M. Ravanelli and A. Borghesi and R. Maroldi and D. Farina},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.04603}
}
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely \texttt{Brixia~score}, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly… Expand
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References

SHOWING 1-10 OF 90 REFERENCES
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
...
1
2
3
4
5
...