[2603.00143] GrapHist: Graph Self-Supervised Learning for Histopathology

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00143 (cs) [Submitted on 24 Feb 2026] Title:GrapHist: Graph Self-Supervised Learning for Histopathology Authors:Sevda Öğüt, Cédric Vincent-Cuaz, Natalia Dubljevic, Carlos Hurtado, Vaishnavi Subramanian, Pascal Frossard, Dorina Thanou View a PDF of the paper titled GrapHist: Graph Self-Supervised Learning for Histopathology, by Sevda \"O\u{g}\"ut and 6 other authors View PDF HTML (experimental) Abstract:Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not originally designed to account for fundamental biological elements of histopathology images, namely cells and their complex interactions. In this work, we hypothesize that a biologically-informed modeling of tissues as cell graphs offers a more efficient representation learning. Thus, we introduce GrapHist, a novel graph-based self-supervised learning framework for histopathology, which learns generalizable and structurally-informed embeddings that enable diverse downstream tasks. GrapHist integrates masked autoencoders and heterophilic graph neural networks that are explicitly designed to capture the heterogeneity of tumor microenvironments. We pre-train GrapHist on a large collection of 11 million cell graphs derived from breast tissues and evaluate its transferability across in- and out-of-domain benchmarks. Our results show that GrapHist achieves competitive...