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

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of
cellular environments at subcellular resolution. However, identifying and characterizing …

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when
performing supervised deep learning, a WSI is divided into small patches, trained and the …

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on
morphological information from histology slides and molecular profiles from genomic data …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

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

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells
that can spread in different parts of the body. According to the World Health Organization …