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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 13·5 min read
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
TL;DR
A comprehensive evaluation of unsupervised domain adaptation (UDA) in medical imaging reveals that while capable models exist, selecting the best one without target labels is challenging. Strategies like ensembling and limited target-labeling can help narrow performance gaps but do not fully resolve them.
✦ Why It Matters
Focus on improving model selection methods to enhance clinical deployment of UDA in medical imaging.
Key Takeaways
- Evaluate multiple UDA algorithms for best performance.
- Implement ensembling techniques to improve model selection.
- Consider a small target-labeling budget for better outcomes.
- Focus on the complete UDA pipeline for clinical applications.
How It Works
The study evaluates UDA by adapting models trained on one domain to perform well in another, unlabeled domain. It combines adaptation techniques with label-free selection methods to identify the most effective models without direct performance metrics from the target domain.