Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Mingchen Gao, Ulas Bagci, Le Lu, Aaron Wu, Mario Buty, Hoo Chang Shin, Holger Roth, Georgios Z. Papadakis, Adrien Depeursinge, Ronald M. Summers, Ziyue Xu*, Daniel J. Mollura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

183 Scopus citations


Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.

Original languageEnglish (US)
Pages (from-to)1-6
Number of pages6
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Issue number1
StatePublished - Jan 2 2018
Externally publishedYes


  • Interstitial lung disease
  • convolutional neural network
  • holistic medical image classification

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Biomedical Engineering
  • Computer Science Applications
  • Computational Mechanics


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