Generative AI enables medical image segmentation in ultra low-data regimes

Li Zhang, Basu Jindal, Ahmed Alaa, Robert Weinreb, David Wilson, Eran Segal, James Zou and Pengtao Xie ()
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Li Zhang: University of California San Diego
Basu Jindal: University of California San Diego
Ahmed Alaa: University of California San Francisco
Robert Weinreb: University of California San Diego
David Wilson: University of Pittsburgh
Eran Segal: Weizmann Institute of Science
James Zou: Stanford University School of Medicine
Pengtao Xie: University of California San Diego

Nature Communications, 2025, vol. 16, issue 1, 1-22

Abstract: Abstract Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation masks. To address this, we propose a generative deep learning framework that produces high-quality image-mask pairs as auxiliary training data. Unlike traditional generative models that separate data generation from model training, ours uses multi-level optimization for end-to-end data generation. This allows segmentation performance to guide the generation process, producing data tailored to improve segmentation outcomes. Our method demonstrates strong generalization across 11 medical image segmentation tasks and 19 datasets, covering various diseases, organs, and modalities. It improves performance by 10–20% (absolute) in both same- and out-of-domain settings and requires 8–20 times less training data than existing approaches. This greatly enhances the feasibility and cost-effectiveness of deep learning in data-limited medical imaging scenarios.

Date: 2025
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DOI: 10.1038/s41467-025-61754-6

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