mlsanityData to 7 Oct 2026

GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation

arXiv 2402.03592 ↗First posted 6 Feb 2024cs.CVcited by 5

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Abstract (arXiv)

Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take advantage of inter- and intra-magnification information contained in WSIs. In this work, we present GRASP, a novel lightweight graph-structured multi-magnification framework for processing WSIs in digital pathology. Our approach is designed to dynamically emulate the pathologist's behavior in handling WSIs and benefits from the hierarchical structure of WSIs. GRASP, which introduces a convergence-based node aggregation mechanism replacing traditional pooling mechanisms, outperforms state-of-the-art methods by a high margin in terms of balanced accuracy, while being significantly smaller than the closest-performing state-of-the-art models in terms of the number of parameters. Our results show that GRASP is dynamic in finding and consulting with different magnifications for subtyping cancers, is reliable and stable across different hyperparameters, and can generalize when using features from different backbones. The model's behavior has been evaluated by two expert pathologists confirming the interpretability of the model's dynamic. We also provide a theoretical foundation, along with empirical evidence, for our work, explaining how GRASP interacts with different magnifications and nodes in the graph to make predictions. We believe that the strong characteristics yet simple structure of GRASP will encourage the development of interpretable, structure-based designs for WSI representation in digital pathology. Data and code can be found in https://github.com/AIMLab-UBC/GRASP

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 40 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

25 of 40 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  7. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  8. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  9. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  10. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  11. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 245
  12. 2020Self supervised contrastive learning for digital histopathologycited by 146
13 more
  1. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  2. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 86
  3. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  4. 2019CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Imagescited by 57
  5. 2021Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slidescited by 52
  6. 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networkscited by 47
  7. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  8. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  9. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  10. 2021DGCN: Diversified Recommendation with Graph Convolutional Networkscited by 29
  11. 2021DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancercited by 27
  12. 2023HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysiscited by 17
  13. 2019Needles in Haystacks: On Classifying Tiny Objects in Large Imagescited by 13

Built on by

Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
  2. 2024A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classificationcited by 12
  3. 2024Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtypingcited by 1
  4. 2025IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysiscited by 1
  5. 2026Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysiscited by 0