A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date: Feb 17, 2026

Abstract

PURPOSE: Cytology is a cornerstone of pathologic diagnosis. However, the use of artificial intelligence (AI) models for cytology-based diagnostics remains constrained by limited data availability and stringent privacy regulations. This study aims to develop COIN, a controllable cytology image generation foundation model, to address these challenges by synthesizing high-quality cytology images to enhance AI diagnostics and support clinical applications. EXPERIMENTAL DESIGN: The COIN model was trained on a large-scale dataset of 112,226 cytology image-report pairs from 16 anatomic sites. Using diagnostic textual reports, it generates high-fidelity cytology images with morphologically and semantically coherent features. Expert cytologists evaluated the generated images for anatomic and diagnostic authenticity. The model's utility was assessed through data augmentation experiments, AI model training under data-scarce conditions, and content-based image retrieval applications. RESULTS: Expert evaluations confirmed the high anatomic and diagnostic fidelity of the images generated by COIN. When used for data augmentation, COIN significantly improved the performance of diagnostic AI models across various tasks. Under data-scarce conditions, models trained exclusively on COIN-generated images demonstrated effective generalization to real-world datasets. Furthermore, COIN supported content-based image retrieval, offering a novel tool for case referencing and clinical decision support. CONCLUSIONS: COIN represents a robust and privacy-preserving framework for scalable cytology data generation. Its ability to synthesize realistic images and enhance AI diagnostics highlights its broad applicability in computational pathology, providing a valuable tool to accelerate the development and implementation of AI-based diagnostic solutions.

Authors

  • Ke Zheng
    Department of Orthopaedics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Xueyi Zheng
    Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Jue Wang
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Xinke Zhang
    Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, 510060, Guangzhou, China.
  • Shiping Chen
    the Cancer Center, Sun Yat-sen Univ., Guangzhou, Guangdong, China.
  • Qunxi Chen
    Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Sha Fu
    School of Information Technology and Management, Hunan University of Finance and Economics, No.139, Section 2, Fenglin Road, Yuelu District, Changsha, 410205, China. [email protected].
  • Dan Xie
  • Ruixuan Wang
    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China. [email protected].
  • Junpeng Lai
    Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Muyan Cai

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