IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysis


Guo Tang (Harbin Institute of Technology Shenzhen), Songhan Jiang (Harbin Institute of Technology Shenzhen), Jinpeng Lu (University of Science and Technology of China), Linghan Cai (Harbin Institute of Technology Shenzhen), Yongbing Zhang (Harbin Institute of Technology Shenzhen)
The 35th British Machine Vision Conference
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Abstract

Pathological images play an essential role in cancer prognosis, while survival analysis, which integrates computational techniques, can predict critical clinical events such as patient mortality or disease recurrence from whole-slide images (WSIs). Recent advancements in multiple instance learning have significantly improved the efficiency of survival analysis. However, existing methods often struggle to balance the modeling of long-range spatial relationships with local contextual dependencies and typically lack inherent interpretability, limiting their clinical utility. To address these challenges, we propose the Interpretable Pathology Graph-Transformer (IPGhormer), a novel framework that captures the characteristics of the tumor microenvironment and models their spatial dependencies across the tissue. IPGhormer uniquely provides interpretability at both tissue and cellular levels without requiring posthoc manual annotations, enabling detailed analyses of individual WSIs and cross-cohort assessments. Comprehensive evaluations on four public benchmark datasets demonstrate that IPGhormer outperforms stateof-the-art methods in both predictive accuracy and interpretability. In summary, our method, IPGhormer offers a promising tool for cancer prognosis assessment, paving the way for more reliable and interpretable decision-support systems in pathology. The code is publicly available at https://anonymous.4open.science/r/IPGPhormer-6EEB.

Citation

@inproceedings{Tang_2025_BMVC,
author    = {Guo Tang and Songhan Jiang and Jinpeng Lu and Linghan Cai and Yongbing Zhang},
title     = {IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysis},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_1061/paper.pdf}
}


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