ArticleNature communications2025
Generative AI enables medical image segmentation in ultra low-data regimes.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Structure-aware 3D diffusion generation for kidney MRI via mask-guided noise scheduling and topology-prior constraints.Scientific reports · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Anatomically constrained liver CT anomaly detection using healthy priors with diffusion-based inpainting.Research square · 2026Article
- Post-MI: unsupervised brain tissue segmentation via post-maximized mutual information.Frontiers in medicine · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Evaluation of Model Performance and Clinical Usefulness in Automated Rectal Segmentation in CT for Prostate and Cervical Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.Bioengineering (Basel, Switzerland) · 2025Article
- MPVT+: a noise-robust training framework for automatic liver tumor segmentation with noisy labels.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.