Corpus ID: 231704294

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

@article{Sriram2021COVID19PV,
  title={COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction},
  author={Anuroop Sriram and Matthew Muckley and Koustuv Sinha and F. Shamout and Joelle Pineau and Krzysztof J Geras and L. Azour and Y. Aphinyanaphongs and N. Yakubova and William Moore},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.04909}
}
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays have been used to predict which patients are most at risk of deterioration. We consider the task of predicting two types of patient deterioration based on chest X-rays: adverse event deterioration (i.e., transfer to the intensive care unit, intubation… Expand
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SHOWING 1-10 OF 40 REFERENCES
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images
  • 15
  • PDF
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
  • 4
  • PDF
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
  • 11
  • PDF
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation
  • 20
  • Highly Influential
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
  • 152
  • Highly Influential
  • PDF
Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication of Patients with COVID-19 from the Emergency Department
  • 2
  • Highly Influential
  • PDF
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
  • 412
  • Highly Influential
  • PDF
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