Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

This paper presents {\em Gem}, a model-agnostic approach for providing interpretable
explanations for any GNNs on various graph learning tasks. Specifically, we formulate the …

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do
not typically consider histopathological features from the tumour microenvironment. Here …

With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …

Breast cancer is the most commonly diagnosed cancer and registers the highest number of
deaths for women. Advances in diagnostic activities combined with large-scale screening …

Explainability of deep learning methods is imperative to facilitate their clinical adoption in
digital pathology. However, popular deep learning methods and explainability techniques …

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by
the relationship between the histopathological structures and the function of the tissue …

Advances in entity-graph analysis of histopathology images have brought in a new
paradigm to describe tissue composition, and learn the tissue structure-to-function …