5 papers · 1 filter
GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI
Julia Alekseenko, Pietro Mascagni, AI4SafeChole Consortium +1
Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices - selecting the "best" glob…
Where are they looking in the operating room?
Keqi Chen, Séraphin Baributsa, Lilien Schewski +5
Purpose: Gaze-following, the task of inferring where individuals are looking, has been widely studied in computer vision, advancing research in visual attention modeling, social sc…
Surg-R1: A Hierarchical Reasoning Foundation Model for Scalable and Interpretable Surgical Decision Support with Multi-Center Clinical Validation
Jian Jiang, Chenxi Lin, Yiming Gu +24
Surgical scene understanding demands not only accurate predictions but also interpretable reasoning that surgeons can verify against clinical expertise. However, existing surgical…
SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
Shi Li, Vinkle Srivastav, Shih-Min Yin +7
Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes. Computer-assisted systems such as…
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16
Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…