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A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images – npj Digital Medicine
- Article
- Open access
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- Peiliang Lou1,
- Yitan Zhu2,
- Nicholas Chia2,
- Roopa Kumari3,
- William Yang3,
- Yan Wang4,
- Brenna C. Novotny1,
- Stacey J. Winham1,
- Ruifeng Guo5,
- Ellen L. Goode1,
- Yajue Huang3,
- Wenchao Han6,
- Tianshu Feng7 &
- …
- Chen Wang1
npj Digital Medicine (2026) Cite this article
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Abstract
Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method across multiple deep learning–based WSI analysis tasks spanning different tissue types. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.
Acknowledgements
This work has been supported by NCI R01 CA248288, an Ovarian SPORE [NCI P50 CA136393] developmental research grant, and by the generous support of Schmidt Sciences and the Susan Morrow Legacy Foundation. The funders had no role in the study design, data analysis, manuscript preparation, or the decision to submit the work for publication.
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Lou, P., Zhu, Y., Chia, N. et al. A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02741-z
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DOI: https://doi.org/10.1038/s41746-026-02741-z
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