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Attention-guided and MIL-constrained CycleGAN for high-fidelity virtual p16 and Ki-67 staining
Xinli Lei1, Yuefeng Xie2, Andi Duan2
1Department of Pathology, Maternal and Child Health Hospital of Ganzhou City, Ganzhou, Jiangxi, China.
Frontiers in Medical Technology
|June 18, 2026
Summary
This study introduces a new AI method to create virtual immunohistochemistry (IHC) stains from standard H&E slides for cervical intraepithelial neoplasia (CIN) grading. The virtual IHC improves diagnostic accuracy and shows promise for clinical use.
Area of Science:
- Digital Pathology
- Computational Biology
- Artificial Intelligence in Medicine
Background:
- Cervical intraepithelial neoplasia (CIN) grading traditionally uses hematoxylin and eosin (H&E) slides alongside immunohistochemistry (IHC) biomarkers like p16 and Ki-67.
- Conventional IHC staining is resource-intensive, requiring significant time, cost, and additional tissue sections, posing limitations for routine diagnostics.
Purpose of the Study:
- To develop a high-fidelity virtual IHC staining generation method from H&E images using an attention-guided, multi-instance learning (MIL) constrained CycleGAN framework.
- To address the limitations of conventional IHC by enabling virtual biomarker expression prediction directly from H&E slides.
Main Methods:
- A weakly paired training strategy was employed, involving whole-slide image registration for H&E and IHC alignment, followed by patch-level pairing.
- The framework integrates attention-guided constraints and MIL-based supervision to ensure structural consistency and reliable biomarker distribution, accounting for spatial misalignments.
- The model was trained and validated using quantitative metrics, expert-based clinical assessment of CIN grading, and cross-domain generalization tests on an external dataset.
Main Results:
- Virtual IHC staining significantly improved diagnostic performance for CIN grading when evaluated by experienced pathologists.
- The proposed method demonstrated superior performance across key metrics including accuracy (ACC), area under the curve (AUC), and kappa scores.
- The framework showed robustness and transferability on an external dataset, with generated staining patterns supported by statistical consistency and biological plausibility.
Conclusions:
- The developed attention-guided MIL-constrained CycleGAN framework offers a practical and clinically relevant solution for virtual IHC generation.
- Virtual IHC staining enhances diagnostic support for CIN grading, showing potential to streamline pathology workflows and reduce costs.
- The method exhibits strong generalization capabilities and addresses concerns regarding false-positive biomarker synthesis, paving the way for wider adoption in digital pathology.
