FedCC: Federated Learning Framework Improves Corpus Callosum Localization in Fetal Ultrasound
A new preprint introduces FedCC, a federated learning framework for localizing the corpus callosum in fetal ultrasound images. FedCC combines a frozen DINOv2 backbone, a lightweight YOLO-based detection head, and LoRA modules to enable parameter-efficient adaptation across multiple clinical sites without sharing patient data. Evaluated on a multi-center dataset, FedCC achieved high accuracy and substantially reduced computational and communication costs compared to traditional approaches.
Why it matters: This work demonstrates a scalable, privacy-preserving AI method that could improve access to accurate fetal neurosonography in resource-limited clinical environments.
Full story at: arXiv Machine Learning ↗