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Graph CNN for Survival Analysis on Whole Slide Pathological Images
Digital Object Identifier
MICCAI 2018. 2018 Sep 26; Volume 11071: Pages pp 174-182

Ruoyu Li
Jiawen Yao
Xinliang Zhu
Yeqing Li
Junzhou Huang


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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