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Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis

Mingxin Liu, Yunzan Liu, Pengbo Xu, Jiquan Ma

TL;DR

GOAT tackles the challenge of gigapixel WSI heterogeneity by modeling WSIs as graphs of patches and applying a geometry-aware transformer to capture spatial relationships in the tumor microenvironment. It introduces MHGA for geometry-aware attention and TAGCN for efficient graph convolution, followed by global attention pooling and a two-layer classifier. On TCGA-NSCLC and TCGA-RCC, GOAT achieves state-of-the-art accuracy and AUC, with ablation studies confirming the contribution of each module and visualization confirming interpretability. This work advances precise cancer subtyping in computational pathology and provides a pathway toward clinically interpretable WSI analysis.

Abstract

The histopathology analysis is of great significance for the diagnosis and prognosis of cancers, however, it has great challenges due to the enormous heterogeneity of gigapixel whole slide images (WSIs) and the intricate representation of pathological features. However, recent methods have not adequately exploited geometrical representation in WSIs which is significant in disease diagnosis. Therefore, we proposed a novel weakly-supervised framework, Geometry-Aware Transformer (GOAT), in which we urge the model to pay attention to the geometric characteristics within the tumor microenvironment which often serve as potent indicators. In addition, a context-aware attention mechanism is designed to extract and enhance the morphological features within WSIs.

Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis

TL;DR

GOAT tackles the challenge of gigapixel WSI heterogeneity by modeling WSIs as graphs of patches and applying a geometry-aware transformer to capture spatial relationships in the tumor microenvironment. It introduces MHGA for geometry-aware attention and TAGCN for efficient graph convolution, followed by global attention pooling and a two-layer classifier. On TCGA-NSCLC and TCGA-RCC, GOAT achieves state-of-the-art accuracy and AUC, with ablation studies confirming the contribution of each module and visualization confirming interpretability. This work advances precise cancer subtyping in computational pathology and provides a pathway toward clinically interpretable WSI analysis.

Abstract

The histopathology analysis is of great significance for the diagnosis and prognosis of cancers, however, it has great challenges due to the enormous heterogeneity of gigapixel whole slide images (WSIs) and the intricate representation of pathological features. However, recent methods have not adequately exploited geometrical representation in WSIs which is significant in disease diagnosis. Therefore, we proposed a novel weakly-supervised framework, Geometry-Aware Transformer (GOAT), in which we urge the model to pay attention to the geometric characteristics within the tumor microenvironment which often serve as potent indicators. In addition, a context-aware attention mechanism is designed to extract and enhance the morphological features within WSIs.
Paper Structure (12 sections, 5 equations, 3 figures, 2 tables)

This paper contains 12 sections, 5 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Graph Construction of Whole Slide Image
  4. Geometry-Aware Transformer
  5. Experimental Setup
  6. Datasets and Evaluation Metrics
  7. Implementation Details
  8. Results and Analysis
  9. Comparison with State-of-the-Art Methods
  10. Ablation Study
  11. Interpretability and Attention Visualization
  12. Conclusion

Figures (3)

  • Figure 1: Overview of the proposed Geometry-Aware Transformer framework.
  • Figure 2: The proposed Multi-Head Geometry Attention.
  • Figure 3: Interpretability and visualization for NSCLC and RCC subtyping. From left to right, the first, second and third column denotes original slide, the attention heatmap, and the top-$k$ highly attended patches with attention scores respectively.