In this paper, we use deep learning techniques to segment different regions from breast cancer histopathology images, such as tumor nucleus, epithelium and stromal areas. Then, in the second stage, the deep segmentation features learned by the neural network are used to predict individual patient survival, using random forest based classification. We show that the deep segmentation network features can predict survival very well, and outperform classical computer vision based shape, texture and other feature descriptors used in earlier research for the same survival prediction task.

The authors have declared no competing interest.

This study did not receive any funding

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