Enhancing Interpretation of Histopathology Whole Slide Image Analysis via Regional Causal Dependency Discovery

Zixian Li, Jun Shi, Zhiguo Jiang, Fengying Xie, Yushan Zheng
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 316:139-149, 2026.

Abstract

Histopathology whole slide image (WSI) analysis is fundamental to computational pathology. Attention-based heatmaps are commonly used for interpretability in WSI analysis. However, heatmap is limited in describing the potential relationships between multiple high-probability regions, which restricts its application in fine-grained WSI analysis tasks. In this paper, we propose Pathology Causal Discovery Network (PCDN), a novel framework that reconstructs interpretable diagnostic pathways by dynamically discovering regional causal dependencies from WSIs. Unlike approaches relying on predefined medical priors, PCDN introduces a Causal Structure Learner (CSL) to infer a Directed Acyclic Graph (DAG) which represents the causal dependencies among pathological regions. A Causal Graph Propagator (CGP) is then designed to guide feature propagation based on the DAG, integrating local causal dependencies with global context. Extensive experiments on three large-scale pathological datasets demonstrate that PCDN achieves state-of-the-art performance and can provide meaningful causal insights for WSI analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-v316-li26a, title = {Enhancing Interpretation of Histopathology Whole Slide Image Analysis via Regional Causal Dependency Discovery}, author = {Li, Zixian and Shi, Jun and Jiang, Zhiguo and Xie, Fengying and Zheng, Yushan}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {139--149}, year = {2026}, editor = {Studer, Linda and Ciompi, Francesco and Khalili, Nadieh and Faryna, Khrystyna and Faryna, Khrystyna and Yeong, Joe and Lau, Mai Chan and Chen, Hao and Liu, Ziyi and Brattoli, Biagio}, volume = {316}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v316/main/assets/li26a/li26a.pdf}, url = {https://proceedings.mlr.press/v316/li26a.html}, abstract = {Histopathology whole slide image (WSI) analysis is fundamental to computational pathology. Attention-based heatmaps are commonly used for interpretability in WSI analysis. However, heatmap is limited in describing the potential relationships between multiple high-probability regions, which restricts its application in fine-grained WSI analysis tasks. In this paper, we propose Pathology Causal Discovery Network (PCDN), a novel framework that reconstructs interpretable diagnostic pathways by dynamically discovering regional causal dependencies from WSIs. Unlike approaches relying on predefined medical priors, PCDN introduces a Causal Structure Learner (CSL) to infer a Directed Acyclic Graph (DAG) which represents the causal dependencies among pathological regions. A Causal Graph Propagator (CGP) is then designed to guide feature propagation based on the DAG, integrating local causal dependencies with global context. Extensive experiments on three large-scale pathological datasets demonstrate that PCDN achieves state-of-the-art performance and can provide meaningful causal insights for WSI analysis.} }
Endnote
%0 Conference Paper %T Enhancing Interpretation of Histopathology Whole Slide Image Analysis via Regional Causal Dependency Discovery %A Zixian Li %A Jun Shi %A Zhiguo Jiang %A Fengying Xie %A Yushan Zheng %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2026 %E Linda Studer %E Francesco Ciompi %E Nadieh Khalili %E Khrystyna Faryna %E Khrystyna Faryna %E Joe Yeong %E Mai Chan Lau %E Hao Chen %E Ziyi Liu %E Biagio Brattoli %F pmlr-v316-li26a %I PMLR %P 139--149 %U https://proceedings.mlr.press/v316/li26a.html %V 316 %X Histopathology whole slide image (WSI) analysis is fundamental to computational pathology. Attention-based heatmaps are commonly used for interpretability in WSI analysis. However, heatmap is limited in describing the potential relationships between multiple high-probability regions, which restricts its application in fine-grained WSI analysis tasks. In this paper, we propose Pathology Causal Discovery Network (PCDN), a novel framework that reconstructs interpretable diagnostic pathways by dynamically discovering regional causal dependencies from WSIs. Unlike approaches relying on predefined medical priors, PCDN introduces a Causal Structure Learner (CSL) to infer a Directed Acyclic Graph (DAG) which represents the causal dependencies among pathological regions. A Causal Graph Propagator (CGP) is then designed to guide feature propagation based on the DAG, integrating local causal dependencies with global context. Extensive experiments on three large-scale pathological datasets demonstrate that PCDN achieves state-of-the-art performance and can provide meaningful causal insights for WSI analysis.
APA
Li, Z., Shi, J., Jiang, Z., Xie, F. & Zheng, Y.. (2026). Enhancing Interpretation of Histopathology Whole Slide Image Analysis via Regional Causal Dependency Discovery. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 316:139-149 Available from https://proceedings.mlr.press/v316/li26a.html.

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