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Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN

  • Maayan Frid-AdarEmail author
  • Idit Diamant
  • Eyal Klang
  • Michal Amitai
  • Jacob Goldberger
  • Hayit Greenspan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection.

Keywords

Liver lesion Detection Convolutional neural network Patch-based system Computer-aided detection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maayan Frid-Adar
    • 1
    Email author
  • Idit Diamant
    • 1
  • Eyal Klang
    • 2
  • Michal Amitai
    • 2
  • Jacob Goldberger
    • 3
  • Hayit Greenspan
    • 1
  1. 1.Department of Biomedical Engineering, Faculty of EngineeringTel Aviv UniversityTel AvivIsrael
  2. 2.Diagnostic Imaging DepartmentSheba Medical CenterTel HashomerIsrael
  3. 3.Faculty of EngineeringBar-Ilan UniversityRamat GanIsrael

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