ArticleClinical cancer research : an official journal of the American Association for Cancer Research2026
A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeCytology 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
designThe 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.
resultsExpert 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.
conclusionsCOIN 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.
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