  Presenting whole slide images (WSIs) as graph will enable a more efficient
and accurate learning framework for cancer diagnosis. Due to the fact that a
single WSI consists of billions of pixels and there is a lack of vast annotated
datasets required for computational pathology, the problem of learning from
WSIs using typical deep learning approaches such as convolutional neural
network (CNN) is challenging. Additionally, WSIs down-sampling may lead to the
loss of data that is essential for cancer detection. A novel two-stage learning
technique is presented in this work. Since context, such as topological
features in the tumor surroundings, may hold important information for cancer
grading and diagnosis, a graph representation capturing all dependencies among
regions in the WSI is very intuitive. Graph convolutional network (GCN) is
deployed to include context from the tumor and adjacent tissues, and
self-supervised learning is used to enhance training through unlabeled data.
More specifically, the entire slide is presented as a graph, where the nodes
correspond to the patches from the WSI. The proposed framework is then tested
using WSIs from prostate and kidney cancers. To assess the performance
improvement through self-supervised mechanism, the proposed context-aware model
is tested with and without use of pre-trained self-supervised layer. The
overall model is also compared with multi-instance learning (MIL) based and
other existing approaches.
