Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
arXiv:2606.16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels. Federated noisy label learning (FNLL) aims to mitigate these effects, yet remains underused in practice as existing evidence is largely based on synthetic noise, simplified settings, and limited real-world noisy evaluation. We address this gap by introducing a benchm
The proliferation of federated learning in sensitive domains like medicine is exposing the critical need for robust methods to handle real-world data imperfections, moving beyond idealized synthetic scenarios.
This benchmark addresses a key practical hurdle for deploying federated AI in healthcare, enabling more accurate and trustworthy medical image analysis without compromising data privacy.
The availability of a real-world benchmark for noisy label learning in federated medical imaging will accelerate the development and adoption of more resilient AI models in clinical settings.
- · Medical AI developers
- · Healthcare institutions
- · Patients (through improved diagnostics)
- · Federated learning platforms
- · Developers relying on synthetic noise only
- · AI models vulnerable to label noise
Improved reliability and wider adoption of federated learning in medical imaging applications.
Increased trust in AI-driven diagnostic tools, potentially leading to faster and more accurate diagnoses.
The benchmark could become a standard, fostering competition and innovation in robust federated AI, potentially extending to other sensitive data domains beyond healthcare.
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Read at arXiv cs.AI