10 citations · 26 across the 11 of their papers we have counts for
6 papers · 1 filter
SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
Sahar Nasirihaghighi, Negin Ghamsarian, Yiping Li +3
Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (F…
Analysis of Transferability Estimation Metrics for Surgical Phase Recognition
Prabhant Singh, Yiping Li, Yasmina Al Khalil
Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data. In surgical video…
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
Tobias Rueckert, David Rauber, Raphaela Maerkl +58
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…
SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition
Yiping Li, Ronald de Jong, Sahar Nasirihaghighi +8
Accurate surgical phase recognition is crucial for computer-assisted interventions and surgical video analysis. Annotating long surgical videos is labor-intensive, driving research…
Scaling up self-supervised learning for improved surgical foundation models
Tim J. M. Jaspers, Ronald L. P. D. de Jong, Yiping Li +12
Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on extensive datasets. However,…
SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Haozheng Xu, Alistair Weld, Chi Xu +16
Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods…