Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910159920431104 |
|---|---|
| author | Kirchner, Max Hoffmann, Hanna Jenke, Alexander C. Saldanha, Oliver L. Pfeiffer, Kevin Kanjo, Weam Alekseenko, Julia de Boer, Claas Kolamuri, Santhi Raj Mazza, Lorenzo Padoy, Nicolas Bano, Sophia Reinke, Annika Maier-Hein, Lena Stoyanov, Danail Kather, Jakob N. Kolbinger, Fiona R. Bodenstedt, Sebastian Speidel, Stefanie |
| author_facet | Kirchner, Max Hoffmann, Hanna Jenke, Alexander C. Saldanha, Oliver L. Pfeiffer, Kevin Kanjo, Weam Alekseenko, Julia de Boer, Claas Kolamuri, Santhi Raj Mazza, Lorenzo Padoy, Nicolas Bano, Sophia Reinke, Annika Maier-Hein, Lena Stoyanov, Danail Kather, Jakob N. Kolbinger, Fiona R. Bodenstedt, Sebastian Speidel, Stefanie |
| contents | Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate solution. The application of FL to complex, spatiotemporal surgical video data remains largely unbenchmarked. We present the FedSurg Challenge, the first international benchmarking initiative dedicated to FL in surgical vision, evaluated as a proof-of-concept on a multi-center laparoscopic appendectomy dataset (preliminary subset of Appendix300). Three submissions were evaluated on generalization to an unseen center and center-specific adaptation. Centralized and Swarm Learning baselines isolate the contributions of task difficulty and decentralization to observed performance. Even with all data pooled centrally, the task achieved only 26.31\% F1-score on the unseen center, while decentralized training introduced an additional, separable performance penalty. Temporal modeling emerges as the dominant architectural factor: video-level spatiotemporal models consistently outperformed frame-level approaches regardless of aggregation strategy. Naive local fine-tuning leads to classifier collapse on imbalanced local data; structured personalized FL with parameter-efficient fine-tuning represents a more principled path toward center-specific adaptation. By characterizing current FL limitations through rigorous statistical analysis, this work establishes a methodological reference point for robust, privacy-preserving AI systems in surgical video analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge Kirchner, Max Hoffmann, Hanna Jenke, Alexander C. Saldanha, Oliver L. Pfeiffer, Kevin Kanjo, Weam Alekseenko, Julia de Boer, Claas Kolamuri, Santhi Raj Mazza, Lorenzo Padoy, Nicolas Bano, Sophia Reinke, Annika Maier-Hein, Lena Stoyanov, Danail Kather, Jakob N. Kolbinger, Fiona R. Bodenstedt, Sebastian Speidel, Stefanie Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate solution. The application of FL to complex, spatiotemporal surgical video data remains largely unbenchmarked. We present the FedSurg Challenge, the first international benchmarking initiative dedicated to FL in surgical vision, evaluated as a proof-of-concept on a multi-center laparoscopic appendectomy dataset (preliminary subset of Appendix300). Three submissions were evaluated on generalization to an unseen center and center-specific adaptation. Centralized and Swarm Learning baselines isolate the contributions of task difficulty and decentralization to observed performance. Even with all data pooled centrally, the task achieved only 26.31\% F1-score on the unseen center, while decentralized training introduced an additional, separable performance penalty. Temporal modeling emerges as the dominant architectural factor: video-level spatiotemporal models consistently outperformed frame-level approaches regardless of aggregation strategy. Naive local fine-tuning leads to classifier collapse on imbalanced local data; structured personalized FL with parameter-efficient fine-tuning represents a more principled path toward center-specific adaptation. By characterizing current FL limitations through rigorous statistical analysis, this work establishes a methodological reference point for robust, privacy-preserving AI systems in surgical video analysis. |
| title | Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.04772 |