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(2018) Large scale automated reading of frontal and lateral chest X-rays using dual convolutional neural networks

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Keyword [PA View & AP View] [Lateral X-ray] [MIMIC-CXR]

Rubin J, Sanghavi D, Zhao C, et al. Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks[J]. arXiv preprint arXiv:1804.07839, 2018.


1. Overview

In this paper, it proposes DualNet

  • PA-lateral pair DualNet
  • AP-lateral pair DualNet
  • experiments on MIMIC-CXR


  • ChestX-ray14 dataset
  • JSRT dataset
  • BSE-JSRT dataset
  • Indiana chest X-ray
  • Shenzhen dataset

1.2. Limitation

  • medical image. 12-bit or greater
  • make no distinction between PA and AP
    • cardiomegaly can only be accurately assessed in PA image
    • AP view will exaggerate the heart silhouette due to magnificention
    • lateral view reveals lung areas that are hidden in the frontal view
    • lateral view can be useful in detecting lower-lobe lung disease, pleural effusions and anterior mediastinal masses

1.3. Network & Details

  • replace 3-channel to 1-channel
  • four denseblock (32 growth rate) per layer
  • no data augmentation
  • Adam with 0.001~0.02 (Triangular2 policy)

1.4. Dataset

  • 80-20-10





  • nearest interpolation (ratio mantained). 512x512

  • normalize from [0, 2^12-1] to [0, 1]

1.5. Results



  • PA results in larger AUC for atelectasis, cardiomegaly, fibrosis, infiltration and pleural thickening
  • lateral benefit for consolidation, edema, effusion, hernia, mass, pneumonia and pneumothorax


1.6. Future Work

  • improvement. data augmentation, pixel normalization
  • patient’s history and current clinical record