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Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection

  • Mingchen GaoEmail author
  • Ziyue Xu
  • Le Lu
  • Adam P. Harrison
  • Ronald M. Summers
  • Daniel J. Mollura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Holistically detecting interstitial lung disease (ILD) patterns from CT images is challenging yet clinically important. Unfortunately, most existing solutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label deep convolutional neural networks (CNNs). The first uses a deep CNN to detect the presence of multiple ILDs using a regression-based loss function. Our second variant further improves performance, using spatially invariant Fisher Vector encoding of the CNN feature activations. We test our algorithms on a dataset of 533 patients using five-fold cross-validation, achieving high area-under-curve (AUC) scores of 0.982, 0.972, 0.893 and 0.993 for Ground Glass, Reticular, Honeycomb and Emphysema, respectively. As such, our work represents an important step forward in providing clinically effective ILD detection.

Keywords

Interstitial lung disease detection Convolutional neural network Multi-label deep regression Unordered pooling Fisher vector encoding 

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Copyright information

© US Government (outside the US) 2016

Authors and Affiliations

  • Mingchen Gao
    • 1
    Email author
  • Ziyue Xu
    • 1
  • Le Lu
    • 1
  • Adam P. Harrison
    • 1
  • Ronald M. Summers
    • 1
  • Daniel J. Mollura
    • 1
  1. 1.Department of Radiology and Imaging SciencesNational Institutes of Health (NIH)BethesdaUSA

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