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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.

Ke Zheng1, Xueyi Zheng1, Jue Wang2,3

  • 1Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|December 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces COIN, a controllable image generation model that creates realistic cytology images. COIN enhances artificial intelligence diagnostics and supports clinical applications, overcoming data limitations and privacy concerns in pathology.

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Area of Science:

  • Computational pathology
  • Medical imaging
  • Artificial intelligence in diagnostics

Background:

  • Cytology is crucial for pathological diagnosis, but AI development is hindered by limited data and privacy regulations.
  • Existing AI diagnostic tools require large, diverse datasets, which are difficult to obtain due to privacy concerns.

Purpose of the Study:

  • To develop COIN, a controllable cytology image generation foundation model.
  • To synthesize high-quality cytology images for enhancing AI diagnostics and supporting clinical applications.
  • To address data scarcity and privacy challenges in AI-driven cytology.

Main Methods:

  • Trained COIN on 112,226 cytology image-report pairs from 16 anatomical sites.
  • Generated high-fidelity cytology images with morphologically and semantically coherent features using diagnostic textual reports.
  • Assessed model utility through expert evaluation, data augmentation, AI model training, and content-based image retrieval.

Main Results:

  • Expert cytologists confirmed the anatomical and diagnostic authenticity of COIN-generated images.
  • COIN significantly improved AI model performance when used for data augmentation.
  • Models trained on COIN images generalized effectively to real-world datasets, even under data-scarce conditions.
  • COIN demonstrated utility in content-based image retrieval for clinical decision support.

Conclusions:

  • COIN provides a robust, privacy-preserving framework for scalable cytology data generation.
  • The model's ability to synthesize realistic images enhances AI diagnostics in computational pathology.
  • COIN is a valuable tool for accelerating AI-based diagnostic solution development and implementation.