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<p>Automated cytogenomic analysis has long been limited by narrow task scope, high annotation demands, and poor robustness to real-world complexity. Here, we introduce CHROMA, the first single-chromosome foundation model for cytogenomics that enables comprehensive, cell-level detection of a wide spectrum of chromosomal abnormalities—including both common and ultra-rare types—in a single, unified framework. Pre-trained on over 4 million chromosomal images from more than 84,000 specimens using self-supervised learning, CHROMA achieves robust and comprehensive detection of numerical and structural abnormalities across diverse classes, dramatically reducing expert annotation workload by 40% through efficient label utilization. The model maintains state-of-the-art accuracy even under highly imbalanced data and challenging imaging conditions, supporting reliable deployment as a risk-aware screening and triage tool, particularly in settings with limited expert availability. An integrated risk-control strategy further ensures safe application by automatically flagging uncertain or rare cases for expert review. By bridging foundational AI advances with real-world clinical needs, CHROMA paves the way for scalable, accessible, and precise cytogenomic analysis in both advanced and underserved healthcare environments.</p>

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A comprehensive foundation model for generalizable cytogenetics in precision oncology with CHROMA

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Changchun Yang,
  • Weiqian Dai,
  • Yilan Zhang,
  • Siyuan Chen,
  • Jingdong Hu,
  • Junkai Su,
  • Yuxuan Chen,
  • Ao Xu,
  • Na Li,
  • Xin Gao,
  • Yongguo Yu

摘要

Automated cytogenomic analysis has long been limited by narrow task scope, high annotation demands, and poor robustness to real-world complexity. Here, we introduce CHROMA, the first single-chromosome foundation model for cytogenomics that enables comprehensive, cell-level detection of a wide spectrum of chromosomal abnormalities—including both common and ultra-rare types—in a single, unified framework. Pre-trained on over 4 million chromosomal images from more than 84,000 specimens using self-supervised learning, CHROMA achieves robust and comprehensive detection of numerical and structural abnormalities across diverse classes, dramatically reducing expert annotation workload by 40% through efficient label utilization. The model maintains state-of-the-art accuracy even under highly imbalanced data and challenging imaging conditions, supporting reliable deployment as a risk-aware screening and triage tool, particularly in settings with limited expert availability. An integrated risk-control strategy further ensures safe application by automatically flagging uncertain or rare cases for expert review. By bridging foundational AI advances with real-world clinical needs, CHROMA paves the way for scalable, accessible, and precise cytogenomic analysis in both advanced and underserved healthcare environments.