Corpus ID: 211066462

Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

@article{Hashir2020QuantifyingTV,
  title={Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays},
  author={Mohammad Hashir and Hadrien Bertrand and Joseph Paul Cohen},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.02582}
}
  • Mohammad Hashir, Hadrien Bertrand, Joseph Paul Cohen
  • Published 2020
  • Engineering, Computer Science, Mathematics
  • ArXiv
  • Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Explore Further: Topics Discussed in This Paper

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 42 REFERENCES
    Do Lateral Views Help Automated Chest X-ray Predictions?
    8
    CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
    213
    MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection
    11
    Emphysema classification using a multi-view convolutional network
    9
    Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning
    53
    Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
    520