GrapHist: Graph Self-Supervised Learning for Histopathology

• URL: http://arxiv.org/abs/2603.00143v1
• Date: Tue, 24 Feb 2026 12:11:49 GMT
• Title: GrapHist: Graph Self-Supervised Learning for Histopathology
• Authors: Sevda Öğüt, Cédric Vincent-Cuaz, Natalia Dubljevic, Carlos Hurtado, Vaishnavi Subramanian, Pascal Frossard, Dorina Thanou,
• Abstract summary: We introduce GrapHist, a novel graph-based self-supervised learning framework for histopathology.<n>GrapHist learns generalizable and structurally-informed embeddings that enable diverse downstream tasks.<n>Our results show that GrapHist achieves competitive performance compared to its vision-based counterparts in slide-, region-hugging, and cell-level tasks.
• Score: 27.886002403669693
• License: http://creativecommons.org/licenses/by/4.0/
• 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 performance compared to its vision-based counterparts in slide-, region-, and cell-level tasks, while requiring four times fewer parameters. It also drastically outperforms fully-supervised graph models on cancer subtyping tasks. Finally, we also release five graph-based digital pathology datasets used in our study at https://huggingface.co/ogutsevda/datasets , establishing the first large-scale graph benchmark in this field. Our code is available at https://github.com/ogutsevda/graphist .

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