Explainable and Position-Aware Learning in Digital Pathology
Milan Aryal Nasim Yahyasoltani
Encoding whole slide images (WSI) as graphs is well motivated since it makes
it possible for the gigapixel resolution WSI to be represented in its entirety
for the purpose of graph learning. To this end, WSIs can be broken into smaller
patches that represent the nodes of the graph. Then, graph-based learning
methods can be utilized for the grading and classification of cancer. Message
passing among neighboring nodes is the foundation of graph-based learning
methods. However, they do not take into consideration any positional
information for any of the patches, and if two patches are found in
topologically isomorphic neighborhoods, their embeddings are nearly similar to
one another. In this work, classification of cancer from WSIs is performed with
positional embedding and graph attention. In order to represent the positional
embedding of the nodes in graph classification, the proposed method makes use
of spline convolutional neural networks (CNN). 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. The identification of cancerous
regions in WSIs is another critical task in cancer diagnosis. In this work, the
explainability of the proposed model is also addressed. A gradient-based
explainbility approach is used to generate the saliency mapping for the WSIs.
This can be used to look into regions of WSI that are responsible for cancer
diagnosis thus rendering the proposed model explainable.