ArticleNature communications2025
General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.
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 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
- A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.Nature communications · 2026Article
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Clinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study.JMIR medical informatics · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- SCA-Net: A Scale- and Contrast-Aware Network for Subtle and Low-Contrast Polyp Segmentation.Journal of imaging informatics in medicine · 2026Article
- Fully automated system predicts osteoporotic vertebral fracture across institutions using lumbar MRI paraspinal muscle signatures.NPJ digital medicine · 2026Article
- Shadow-Calibrated Stereo Vision for Colorimetric Sweat Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.Nature communications · 2026Article
- Vision-language framework for multi-sequence brain magnetic resonance imaging.medRxiv : the preprint server for health sciences · 2026Article
- DGCNet: a clinical knowledge-driven model for the segmentation of reflux esophagitis in endoscopic images.Gastroenterology report · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- EfficientSegNet: Lightweight Semantic Segmentation with Multi-Scale Feature Fusion and Boundary Enhancement.Sensors (Basel, Switzerland) · 2025Article
- Risk stratification for early-stage NSCLC progression: a federated learning framework with large-small model synergy.Frontiers in oncology · 2025Article
- Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography.IEEE access : practical innovations, open solutions · 2025Article
- Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The foundation model, trained on extensive and diverse datasets, has shown strong performance across numerous downstream tasks. Nevertheless, its application in the medical domain is significantly hindered by issues such as data volume, heterogeneity, and privacy concerns. Therefore, we propose the Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates the decentralized construction of expert clinical models for various medical tasks. The VFMGL framework transfers general knowledge from large-parameter vision foundation models to construct lightweight, robust expert clinical models tailored to specific medical tasks. Through extensive experiments and analyses across a range of medical tasks and scenarios, we demonstrate that VFMGL achieves superior performance in both medical image classification and segmentation tasks, effectively managing the challenges posed by data heterogeneity. These results underscore the potential of VFMGL in advancing the efficacy and reliability of AI-driven medical diagnostics.
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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.