mlsanityData to 7 Oct 2026

Graph Neural Networks in Histopathology: Emerging Trends and Future Directions

arXiv 2406.12808 ↗First posted 18 Jun 2024cs.CV cs.AI cs.LGcited by 13

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Abstract (arXiv)

Histopathological analysis of Whole Slide Images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fall short in capturing the intricate spatial dependencies inherent in WSIs. Graph Neural Networks (GNNs) present a promising alternative, adept at directly modeling pairwise interactions and effectively discerning the topological tissue and cellular structures within WSIs. Recognizing the pressing need for deep learning techniques that harness the topological structure of WSIs, the application of GNNs in histopathology has experienced rapid growth. In this comprehensive review, we survey GNNs in histopathology, discuss their applications, and explore emerging trends that pave the way for future advancements in the field. We begin by elucidating the fundamentals of GNNs and their potential applications in histopathology. Leveraging quantitative literature analysis, we identify four emerging trends: Hierarchical GNNs, Adaptive Graph Structure Learning, Multimodal GNNs, and Higher-order GNNs. Through an in-depth exploration of these trends, we offer insights into the evolving landscape of GNNs in histopathological analysis. Based on our findings, we propose future directions to propel the field forward. Our analysis serves to guide researchers and practitioners towards innovative approaches and methodologies, fostering advancements in histopathological analysis through the lens of graph neural networks.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 65 of its 179 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

65 of 179 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 3.

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  1. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  2. 2025Continuous Simplicial Neural Networkscited by 2
  3. 2026GrapHist: Graph Self-Supervised Learning for Histopathologycited by 2
  4. 2025A Graph-Based Framework for Interpretable Whole Slide Image Analysiscited by 1
  5. 2025SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathologycited by 1
  6. 2026SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentationcited by 1
  7. 2025Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusioncited by 0
  8. 2025A Novel Approach to Linking Histology Images with DNA Methylationcited by 0
  9. 2025HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classificationcited by 0
  10. 2025FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classificationcited by 0
  11. 2025Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Frameworkcited by 0
  12. 2025PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathologycited by 0
1 more
  1. 2026LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Imagescited by 0