Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges

2025-05-16

AI Summary

This paper systematically reviews advances and clinical translation challenges of generative models in computational pathology. Addressing three core problems—low-fidelity whole-slide image (WSI) generation, weak clinical interpretability, and insufficient regulatory compliance of synthetic data—the authors propose a novel four-dimensional taxonomy encompassing GANs, diffusion models, foundation models, and multimodal modeling. They identify critical bottlenecks in domain adaptation of diffusion models and vision-language foundation models to pathology. Furthermore, they outline a development pathway toward clinically deployable, multimodal unified generative systems. Based on technical evolution and applicability analysis of over 150 studies, the work establishes an authoritative evaluation benchmark and a translational roadmap. It provides both theoretical foundations and practical guidelines for algorithmic innovation and real-world clinical deployment.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation 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, multimodal image-text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. 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 unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology.

Problem

• Survey generative models in computational pathology methods and applications
• Analyze challenges in high-fidelity image generation and clinical interpretability
• Explore ethical and legal implications of synthetic data usage

Innovation

• Generative modeling for data-efficient learning and augmentation
• Multimodal image-text generation in computational pathology
• Diffusion models and foundation models for generative tasks