Zhang, Y., Zhang, X., Qi, X., Wu, X., Chen, F., Yang, G., & Fu, H. (2025). Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges. IEEE Reviews in Biomedical Engineering, 1–22. https://doi.org/10.1109/rbme.2025.3619086
Abstract:
Content generation modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and task-oriented generation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, molecular profile–morphology generation, and other specialized generation applications. By analyzing over 150 representative studies, we trace the evolution of content generation architectures—from early generative adversarial networks to recent advances in diffusion models and generative vision–language models. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing integrated and clinically deployable generation systems. This work aims to provide a foundational reference for researchers and practitioners developing content generation models in computational pathology.
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Funding Info:
There was no specific funding for the research done