# HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

arXiv: https://arxiv.org/abs/2309.07400 · HTML: https://mlsanity.com/paper/2309.07400/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Representing Long-Range Context for Graph Neural Networks with Global Attention](https://mlsanity.com/paper/2201.08821/) (2022)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks](https://mlsanity.com/paper/2107.13048/) (2021)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides](https://mlsanity.com/paper/2101.07903/) (2021)
- [Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction](https://mlsanity.com/paper/2205.06672/) (2022)

## Built on by (most cited first, 17 of 17)

- [PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology](https://mlsanity.com/paper/2502.08916/) (2025)
- [AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification](https://mlsanity.com/paper/2406.15303/) (2024)
- [RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification](https://mlsanity.com/paper/2403.10858/) (2024)
- [ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2603.06658/) (2026)
- [GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation](https://mlsanity.com/paper/2402.03592/) (2024)
- [Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images](https://mlsanity.com/paper/2404.10894/) (2024)
- [FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification](https://mlsanity.com/paper/2407.07340/) (2024)
- [Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans](https://mlsanity.com/paper/2510.10779/) (2025)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis](https://mlsanity.com/paper/2506.16398/) (2025)
- [Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans](https://mlsanity.com/paper/2508.01045/) (2025)
- [Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping](https://mlsanity.com/paper/2407.18105/) (2024)
- [DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer](https://mlsanity.com/paper/2506.12982/) (2025)
- [Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation](https://mlsanity.com/paper/2605.05164/) (2026)
- [Spatial Blindness in Whole-Slide Multiple Instance Learning](https://mlsanity.com/paper/2605.17449/) (2026)
- [GCE-MIL: Faithful and Recoverable Evidence for Multiple Instance Learning in Whole-Slide Imaging](https://mlsanity.com/paper/2605.17456/) (2026)
- [From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology](https://mlsanity.com/paper/2608.03508/) (2026)

## Abstract (arXiv)

In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments. This hierarchical structure is believed to be beneficial for cancer diagnosis and prognosis tasks. However, most previous hierarchical WSI analysis works (1) only characterize local or global correlations within the WSI pyramids and (2) use only unidirectional interaction between different resolutions, leading to an incomplete picture of WSI pyramids. To this end, this paper presents a novel Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With Graph Neural Network and Transformer as the building commons, HIGT can learn both short-range local information and long-range global representation of the WSI pyramids. Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids. Finally, we aggregate both coarse-grained and fine-grained features learned from different levels together for slide-level prediction. We evaluate our methods on two public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and esophageal carcinoma (ESCA). Experimental results show that our HIGT outperforms both hierarchical and non-hierarchical state-of-the-art methods on both tumor subtyping and staging tasks.