POSITION-AWARE GRAPH-BASED LEARNING OF WHOLE SLIDE IMAGES

Encoding whole slide images (WSI) as graphs is well motivated as it allows the gigapixel resolution WSI to be represented in its entirety for learning. To this end, WSIs can be divided into small patches representing the nodes of a graph. Then, graph-based learning approaches can be deployed for cancer grading and classification. Graph-based learning methods such as graph neural network (GNN) are based on message passing among neighboring nodes. However, they do not account for positional information for each patch and if two patches are located in topologically isomorphic neighborhoods, their embedding is almost identical. In this work, classification of cancer from WSI is performed with positional embedding and graph attention. The proposed approach is based on spline convolutional neural network (CNN) to encode the positional embedding of the nodes in graph classification. The algorithm is then tested with the WSI dataset for grading prostate cancer and kidney cancer. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance.