Unregistered Bosniak Classification with Multi-phase Convolutional Neural Networks

  • Myunggi Lee
  • Hyeogjin Lee
  • Jiyong Oh
  • Hak Jong Lee
  • Seung Hyup Kim
  • Nojun KwakEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)


Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks. Despite its success, deep learning applied to medical image analysis has not been wholly explored. In this paper, we study on convolutional neural network (CNN) architectures applied to a Bosniak classification problem to classify Computed Tomography images into five Bosniak classes. We use a new medical image dataset called as the Bosniak classification dataset which will be fully introduced in this paper. For this data set, we employ a multi-phase CNN approach to predict classification accuracy. We also discuss the representation power of CNN compared to previously developed features (Garbor features) in medical image. In our experiment, we use data combination method to enlarge the data set to avoid overfitting problem in multi-phase medical imaging system. Using multi-phase CNN and data combination method we proposed, we have achieved 48.9 % accuracy on our test set, which improves the hand-crafted features by 11.9 %.


Medical image Bosniak classification Deep convolutional neural network Unregistered medical image 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Myunggi Lee
    • 1
  • Hyeogjin Lee
    • 1
  • Jiyong Oh
    • 1
  • Hak Jong Lee
    • 2
  • Seung Hyup Kim
    • 2
  • Nojun Kwak
    • 1
    Email author
  1. 1.Graduate School of Convergence Science and TechnologySeoul National UniversitySeoulKorea
  2. 2.Department of Radiology, College of MedicineSeoul National UniversitySeoulKorea

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