Corpus ID: 59523590

Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System

@article{Cohen2019ChesterAW,
  title={Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System},
  author={Joseph Paul Cohen and P. Bertin and Vincent Frappier},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.11210}
}
  • Joseph Paul Cohen, P. Bertin, Vincent Frappier
  • Published 2019
  • Computer Science, Biology
  • ArXiv
  • Deep learning has shown promise to augment radiologists and improve the standard of care globally. [...] Key Method The system is designed to be used as a reference where a user can process an image to confirm or aid in their diagnosis. The system contains three main components: out-of-distribution detection, disease prediction, and prediction explanation. The system open source and freely available here: this https URLExpand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 41 REFERENCES
    CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
    • 702
    • Highly Influential
    • PDF
    PadChest: A large chest x-ray image dataset with multi-label annotated reports
    • 56
    • Highly Influential
    • PDF
    Deep Learning: A Primer for Radiologists.
    • 201
    MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
    • 69
    Prediction gradients for feature extraction and analysis from convolutional neural networks
    • 5
    • PDF
    Deep Learning
    • 12,647
    • PDF