Causal Shortcut Reduction for Stable Multi-Center Histopathology Image Diagnosis

Histopathology image diagnosis is vulnerable to shortcut learning because neural networks may capture staining protocols, scanner artifacts, tissue preparation differences, or hospital-specific color distributions rather than disease-related cellular morphology. This study examines causal shortcut reduction for robust tumor classification across heterogeneous pathology centers. We propose a Causal Morphology Constraint Network (CMCN), which identifies shortcut-sensitive features through inter-center risk variation, stain perturbation response, and patch-level attribution consistency. The model then suppresses shortcut dependence using morphology-preserving counterfactual augmentation and an invariant risk penalty across acquisition environments. Experiments were conducted on 86,420 whole-slide image patches collected from 12,760 patients across five medical centers, covering benign tissue, low-grade lesions, and malignant tumors. External validation used two unseen hospitals with different staining machines and scanning resolutions. Compared with a standard DenseNet-121 model, CMCN improved external AUC from 0.781 to 0.846 and reduced the performance gap between internal and external testing from 12.8% to 5.3%. Under synthetic stain shift, classification accuracy decreased by only 3.9%, compared with 14.6% for the baseline model. These results suggest that causal shortcut reduction can improve diagnostic stability in pathology image analysis.

1. Zhang, Z. (2026). A Study on the Impact of Cost Presentation on Decision-making Bias and Cancellation Behavior in Online Subscriptions. Available at SSRN 7349839.
2. Jalil, M. A., & Zafra, A. (2025). Deep Learning in Multiple Instance Learning: Methods, Applications and Research Trends. IEEE Access.
3. Du, Y., Chen, Z., Liu, Q., & Dong, Y. (2026). Co-identification of Vibration, Structural Looseness, and Surface Damage in Industrial Equipment Operation Videos.
4. Zheng, J., & Makar, M. (2022). Causally motivated multi-shortcut identification and removal. Advances in Neural Information Processing Systems, 35, 12800-12812.
5. Mini Han Wang , " AI-Powered Innovations in Ophthalmic Diagnosis and Treatment ", Bentham Science Publishers (2025). https://doi.org/10.2174/97988988122871250101.
6. Xiong, W., Zeng, Y., & Guo, Y. (2026). A Study on the Characterization of Building MEP System States and the Learning of Behavioral Patterns Driven by Large-Scale Operational Data. Available at SSRN 7121883.
7. Hong, Z., Liang, S., Xu, T., & Chen, H. (2026). A Study on Failover Verification and Recovery Objective Prediction for Cross-Region Cloud Services.
8. Wu, J., Zhang, J., Wu, D., & Peng, Y. (2026). Visual Transformation Mechanisms for Integrating Digital Cultural Heritage Resources into Junior High School Art Curricula.
9. Han, Z., Chen, W., Han, Y., Mao, R., & Qin, J. (2026). Fast Diversified Top-k Rule Discovery via User-Guided Embeddings. IEEE Transactions on Knowledge and Data Engineering.
10. Huang, J., Xu, T., Yang, J., & Yin, J. (2026). A Graph Neural Network Approach for Financial Data Validation and Risk Identification in Large-Scale Intercompany Transactions. Available at SSRN 7175058.
11. Zhang, Z., Wang, J., Li, Z., Wang, Y., & Zheng, J. (2025). Anncoder: A mti-agent-based code generation and optimization model. Symmetry, 17(7), 1087.
12. Gu, X. (2026). Identifying Causal Effects and Analyzing Heterogeneity of User Growth Interventions on Digital Platforms: Evidence from Large-Scale Behavioral Data. Available at SSRN 6809181.
13. Quan, Q., Gao, Y., & Wang, Q. (2025). The impact of teacher emotional support on students' engagement in AI-mediated English learning environments: The mediating role of resilience and self-efficacy. Acta Psychologica, 260, 105766.
14. Zhao, J., Fan, J., & Li, L. (2026). A Study on an Explainable Causal-Enhanced LLM Agent for Predicting the Forming Quality of Automotive Component Materials.
15. You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.
16. Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). Retrieval-Guided Structured Reasoning and Interpretable Representation Learning for Large-Scale Video–Language Models.
17. Wang, Z., Yu, J., Liu, H., Zheng, Z., Jin, Y., Li, S., ... & Zhang, K. (2025, July). Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 6909-6920).
18. Liang, S., Du, Y., Chen, W., & Liu, Z. (2026). Order Allocation and Emergency Transportation Decisions Amid Multi-Source Procurement Disruptions.
19. Bao, Y., & Wang, H. (2026). Orion: A High-Throughput, Fault-Tolerant Event Routing Architecture for Hyperscale Data Streams. Fault-Tolerant Event Routing Architecture for Hyperscale Data Streams (January 01, 2026).
20. Wang, S., Feng, Y., & Fang, X. (2026, May). A Large Language Model-Enabled Multi-Agent Collaboration Method for Complex Task Solving. In 2026 6th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 253-256). IEEE.
21. Xu, Y., Sun, Y., Zhai, B., Li, M., Liang, W., Li, Y., & Du, S. (2025, April). Zero-shot video moment retrieval via off-the-shelf multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 9, pp. 8978-8986).
22. Yang, Q., & Du, Y. (2026). Predicting Rollback Risks and Controlling Gradual Rollout Traffic for Online Ranking Feature Deployments.
23. Zhao, Z., & Welsch, R. E. (2024). Hierarchical reinforced trader (hrt): A bi-level approach for optimizing stock selection and execution. arXiv preprint arXiv:2410.14927.
24. Zheng, X., Chen, X., Gong, S., Griffin, X., & Slabaugh, G. (2025, September). Xfmamba: Cross-fusion mamba for multi-view medical image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 672-682). Cham: Springer Nature Switzerland.
25. Liang, S., Hsu, K. H., & Liu, Z. (2026). Rolling Scheduling and Capacity Balancing for Server Assembly Under Engineering Change Disturbances.
26. Yuan, Y., Xu, T., Yin, J., & Huang, J. (2026). Modeling Loading Anomalies and Identifying Root Causes in Media Consumption Workflows on Large Social Media Platforms. Available at SSRN 7393318.
27. Su, J., Cai, C., Zhu, F., He, C., Xu, X., Guan, D., & Si, C. (2024, September). Momentum auxiliary network for supervised local learning. In European Conference on Computer Vision (pp. 276-292). Cham: Springer Nature Switzerland.
28. Bao, Y., & Wang, H. (2026). Large-scale Metrics Migration: A Systematic Approach to Rebuilding Production Measurement Infrastructure. Available at SSRN 7185660.
29. Ceylan, M., Tabin, A., Langer, P., Fleisch, E., & Barata, F. (2026). EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography. arXiv preprint arXiv:2601.10356.
30. Yang, Z., Alexandrova, A. N., & Sautet, P. (2026). Modeling CO2 Hydrogenation to Methanol on an Ensemble of Inverse ZrO2 on Cu Catalytic Sites: Mechanism, Reactivity, and Deactivation. Angewandte Chemie, e5448247.
31. Lyu, Y., Armas-Cervantes, A., & Mendoza, A. (2025, December). Uncovering self-regulated learning patterns in Chatbot-powered environment through a process mining lens. In 36th Australasian Association for Engineering Education Annual Conference (AAEE2025) (pp. 371-379). Brisbane, QLD: Engineers Australia.
32. Liang, J., Xiao, Y., Yang, H., Yu, Z., Liu, J. N., & Yang, K. (2026, April). C-HyPOD: Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement for Graph Out-of-Distribution Recommendation. In Proceedings of the ACM Web Conference 2026 (pp. 6541-6550).
33. Xi, X., Chen, P., Qiu, Y., Luo, R., Tong, P., & Liang, J. Video-BCI: Bayesian Cognitive Integration of Self-Prior Hypotheses for Video Understanding. In Forty-third International Conference on Machine Learning.
34. Xu, Z., Zhang, X., Li, R., Tang, Z., Huang, Q., & Zhang, J. (2024). Fakeshield: Explainable image forgery detection and localization via multi-modal large language models. arXiv preprint arXiv:2410.02761.
35. Jia, Z., Huang, C., Wang, Z., Fei, H., Wu, S., & Feng, J. (2024). Finger recovery transformer: toward better incomplete fingerprint identification. IEEE Transactions on Information Forensics and Security, 19, 8860-8874.
36. Huang, C., Jia, Z., Fei, H., Zhu, Y., Yuan, Z., Zhang, J., & Zhou, J. (2025, October). ArtFRD: A Fisher-Rao Mixture Metric for Generative Model Aesthetic Evaluation. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 6654-6662).
37. Cui, J., Zhang, G., Chen, Z., & Yu, N. (2022). Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics. Scientific Reports, 12(1), 22349.
38. Jiang, C. (2026). Information doesn’t work? Testing experiential drivers of VR application in heritage tourism by affective-cognitive pathways. Journal of Hospitality and Tourism Technology, 1-28.
39. Wang, T., & Xia, Z. (2025). Stability of In-Context Learning: A Spectral Coverage Perspective. arXiv preprint arXiv:2509.20677.
40. Chaoyong Ma, Nan Si, Xuewei Song, Chen Liang and Haoming Luo, A Bearing Fault Diagnosis Method Based on Concise Empirical Wavelet Transform, International Journal of Comprehensive Engineering，2023, 12(1), 1-6
41. Yuyan Li, Lin Chen, Mengyu Yang, Qiaomei Li and Taifu Li, Polarization Converters Composed of Metasurfaces with Independent Manipulation of Polarization and Phase, International Journal of Comprehensive Engineering，2023, 12(1), 7-14
42. Jiaxun Du, Zhenbao Fu, Jiachen Liu, Huaqing Wang, Zhentao Ge and Liuyang Song, Bearing Fault Diagnosis Method Based on Attention Residual Network Under Small Sample Condition, International Journal of Comprehensive Engineering，2023, 12(1), 15-24