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 …

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML)
algorithms are widely used for solving critical problems in bioinformatics, biomedical …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Abstract Large Vision-Language Models (LVLMs) are capable of handling diverse data
types such as imaging, text, and physiological signals, and can be applied in various fields …

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

Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive
factor which is overexpressed in 15–20% of breast cancer (BCa). The determination of its …

Understanding the spatial heterogeneity of tumours and its links to disease initiation and
progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

Deep graph learning (DGL) has achieved remarkable progress in both business and
scientific areas ranging from finance and e-commerce, to drug and advanced material …

Abstract Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …