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.
@article{arxiv.2501.16992,
title = {FedEFM: Federated Endovascular Foundation Model with Unseen Data},
author = {Tuong Do and Nghia Vu and Tudor Jianu and Baoru Huang and Minh Vu and Jionglong Su and Erman Tjiputra and Quang D. Tran and Te-Chuan Chiu and Anh Nguyen},
journal= {arXiv preprint arXiv:2501.16992},
year = {2025}
}