FedEFM: Federated Endovascular Foundation Model with Unseen Data

2025-01-28

AI Summary

To address the dual challenges of scarce annotations and non-shareable cross-institutional data in X-ray catheter/guidewire segmentation for endovascular interventions, this paper proposes the first foundation model framework tailored for vascular interventional imaging under a federated learning paradigm. Methodologically, it integrates federated learning with foundation model pretraining and introduces a novel differentiable Earth Mover’s Distance–based knowledge distillation mechanism to mitigate representation degradation caused by client-wise local data distribution shifts. Evaluated on multiple downstream few-shot segmentation tasks, the framework achieves state-of-the-art performance, significantly improving fine-tuning efficiency and generalization across institutions. This work establishes a practical, scalable, and privacy-preserving paradigm for AI modeling in sensitive medical imaging applications.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Segmentation
• Intelligent Robots: Other Foundations and Applications

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting catheter and guidewire structures is challenging due to the limited availability of labeled data. Foundation models offer a promising solution by enabling the collection of similar domain data to train models whose weights can be fine-tuned for downstream tasks. Nonetheless, large-scale data collection for training is constrained by the necessity of maintaining patient privacy. This paper proposes a new method to train a foundation model in a decentralized federated learning setting for endovascular intervention. To ensure the feasibility of the training, we tackle the unseen data issue using differentiable Earth Mover's Distance within a knowledge distillation framework. Once trained, our foundation model's weights provide valuable initialization for downstream tasks, thereby enhancing task-specific performance. Intensive experiments show that our approach achieves new state-of-the-art results, contributing to advancements in endovascular intervention and robotic-assisted endovascular surgery, while addressing the critical issue of data sharing in the medical domain.

Problem

• Catheter and Wire Recognition
• Limited Annotated Images
• Patient Privacy Preservation

Innovation

• Federated Learning
• Vascular Intervention
• Data Privacy