An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology

2025-05-21

AI Summary

Chromosome abnormality identification is critical for genetic disorder diagnosis and precision oncology, yet existing AI approaches suffer from high annotation costs, incomplete dataset coverage, and poor generalizability. To address these limitations, we propose CHROMA—the first inclusive foundation model designed for comprehensive chromosome abnormality detection across all major types, enabling robust generalization under resource-constrained settings (e.g., few-shot learning and severe class imbalance). CHROMA leverages self-supervised learning, pretrained on 84,000 clinical specimens (~4 million high-resolution karyotype images), and integrates multi-scale feature representation with anomaly-decoupled modeling. Extensive evaluations across diverse chromosome abnormality detection tasks demonstrate consistent and significant superiority over state-of-the-art methods. CHROMA markedly reduces reliance on expert annotations, enhances early detection of rare genomic aberrations, and advances the clinical deployment of scalable, trustworthy AI in cytogenetics.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Other Foundations of Human Computation & AI
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aberrations. However, developing an AI model are hindered by the overwhelming complexity and diversity of chromosomal abnormalities, requiring extensive annotation efforts, while automated methods remain task-specific and lack generalizability due to the scarcity of comprehensive datasets spanning diverse resource conditions. Here, we introduce CHROMA, a foundation model for cytogenomics, designed to overcome these challenges by learning generalizable representations of chromosomal abnormalities. Pre-trained on over 84,000 specimens (~4 million chromosomal images) via self-supervised learning, CHROMA outperforms other methods across all types of abnormalities, even when trained on fewer labelled data and more imbalanced datasets. By facilitating comprehensive mapping of instability and clonal leisons across various aberration types, CHROMA offers a scalable and generalizable solution for reliable and automated clinical analysis, reducing the annotation workload for experts and advancing precision oncology through the early detection of rare genomic abnormalities, enabling broad clinical AI applications and making advanced genomic analysis more accessible.

Problem

• Addressing complexity and diversity of chromosomal abnormalities in AI models
• Overcoming task-specific limitations and lack of generalizability in automated methods
• Reducing expert annotation workload for reliable clinical genomic analysis

Innovation

• Self-supervised learning on 84,000 specimens
• Generalizable chromosomal abnormality representations
• Scalable solution for clinical genomic analysis