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<p>Data scarcity, inter-institutional stain variability, and privacy constraints are major challenges impeding the development of generalizable artificial intelligence models in digital pathology. Although recent generative adversarial network (GAN)-based synthesis approaches show promise, they struggle to preserve fine-grained nuclear morphology or maintain class-specific histological diversity. Moreover, existing diffusion-based studies have not sufficiently addressed class-conditional synthesis across heterogeneous, multi-institutional pathology data. We introduce the Efficient Pathology Diffusion Pipeline (EPDP), a class-conditional diffusion framework that enables the generation of clinically relevant, subtype-specific synthetic histopathology images for training and validating diagnostic AI models. By providing high-fidelity, subtype-specific synthetic datasets, EPDP lowers the barriers to developing robust AI diagnostic tools and supports the establishment of standardized evaluation frameworks in digital pathology. To achieve this, EPDP integrates a customized denoising U-Net that employs nuclear details, learnable class embeddings, and CycleGAN-based stain normalization with reference-guided alignment. This design explicitly targets the preservation of fine-grained nuclear morphology, class-specific histological diversity, and stain-invariant visual consistency across institutions. Multi-institutional hematoxylin and eosin whole-slide images are curated via a two-round pathology review to build subtype-labeled training and evaluation sets. Image fidelity is assessed using FID, 1–LPIPS, and SSIM, and compared against state-of-the-art models including PathDiff, PathLDM, and DiffInfinite. Diagnostic utility was evaluated using cross-domain classification (EfficientNetV2-L), and perceptual realism was assessed using a visual Turing test (VTT). The real–synthetic FID was lower than that of real–real FID by 11.0% (breast) and 8.6% (gastric). The subtype 1–LPIPS gaps were ≤ 12%, and the SSIM gaps were ≤ 0.3%. Classifiers trained only on synthetic patches matched real-trained baselines within an ~ 1% F1 score on real-image validation. Pathologists performed at near-chance levels in the VTT, with accuracies ranging from 50% to 56%.</p>

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Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • So Hyeon Lee,
  • Young Seop Lee,
  • Young Jae Kim,
  • Jisup Kim,
  • Kwang Gi Kim

摘要

Data scarcity, inter-institutional stain variability, and privacy constraints are major challenges impeding the development of generalizable artificial intelligence models in digital pathology. Although recent generative adversarial network (GAN)-based synthesis approaches show promise, they struggle to preserve fine-grained nuclear morphology or maintain class-specific histological diversity. Moreover, existing diffusion-based studies have not sufficiently addressed class-conditional synthesis across heterogeneous, multi-institutional pathology data. We introduce the Efficient Pathology Diffusion Pipeline (EPDP), a class-conditional diffusion framework that enables the generation of clinically relevant, subtype-specific synthetic histopathology images for training and validating diagnostic AI models. By providing high-fidelity, subtype-specific synthetic datasets, EPDP lowers the barriers to developing robust AI diagnostic tools and supports the establishment of standardized evaluation frameworks in digital pathology. To achieve this, EPDP integrates a customized denoising U-Net that employs nuclear details, learnable class embeddings, and CycleGAN-based stain normalization with reference-guided alignment. This design explicitly targets the preservation of fine-grained nuclear morphology, class-specific histological diversity, and stain-invariant visual consistency across institutions. Multi-institutional hematoxylin and eosin whole-slide images are curated via a two-round pathology review to build subtype-labeled training and evaluation sets. Image fidelity is assessed using FID, 1–LPIPS, and SSIM, and compared against state-of-the-art models including PathDiff, PathLDM, and DiffInfinite. Diagnostic utility was evaluated using cross-domain classification (EfficientNetV2-L), and perceptual realism was assessed using a visual Turing test (VTT). The real–synthetic FID was lower than that of real–real FID by 11.0% (breast) and 8.6% (gastric). The subtype 1–LPIPS gaps were ≤ 12%, and the SSIM gaps were ≤ 0.3%. Classifiers trained only on synthetic patches matched real-trained baselines within an ~ 1% F1 score on real-image validation. Pathologists performed at near-chance levels in the VTT, with accuracies ranging from 50% to 56%.