FedEFM: Federated Endovascular Foundation Model with Unseen Data

• URL: http://arxiv.org/abs/2501.16992v1
• Date: Tue, 28 Jan 2025 14:46:38 GMT
• Title: FedEFM: Federated Endovascular Foundation Model with Unseen Data
• Authors: Tuong Do, Nghia Vu, Tudor Jianu, Baoru Huang, Minh Vu, Jionglong Su, Erman Tjiputra, Quang D. Tran, Te-Chuan Chiu, Anh Nguyen,
• Abstract summary: This paper proposes a new method to train a foundation model in a decentralized federated learning setting for endovascular intervention. We tackle the unseen data issue using differentiable Earth Mover's Distance within a knowledge distillation framework. Our approach achieves new state-of-the-art results, contributing to advancements in endovascular intervention and robotic-assisted surgery.
• Score: 11.320026809291239
• License:
• 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.

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