Graph CNN for Survival Analysis on Whole Slide Pathological Images

  • Ruoyu Li
  • Jiawen Yao
  • Xinliang Zhu
  • Yeqing Li
  • Junzhou HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Deep neural networks have been used in survival prediction by providing high-quality features. However, few works have noticed the significant role of topological features of whole slide pathological images (WSI). Learning topological features on WSIs requires dense computations. Besides, the optimal topological representation of WSIs is still ambiguous. Moreover, how to fully utilize the topological features of WSI in survival prediction is an open question. Therefore, we propose to model WSI as graph and then develop a graph convolutional neural network (graph CNN) with attention learning that better serves the survival prediction by rendering the optimal graph representations of WSIs. Extensive experiments on real lung and brain carcinoma WSIs have demonstrated its effectiveness.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ruoyu Li
    • 1
    • 2
  • Jiawen Yao
    • 1
    • 2
  • Xinliang Zhu
    • 1
    • 2
  • Yeqing Li
    • 1
    • 2
  • Junzhou Huang
    • 1
    • 2
    Email author
  1. 1.Department of Computer Science and EngineeringThe University of Texas at ArlingtonArlingtonUSA
  2. 2.Tencent AI LabShenzhenChina

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