5 papers
StepAL: Step-aware Active Learning for Cataract Surgical Videos
Nisarg A. Shah, Bardia Safaei, Shameema Sikder +2
Active learning (AL) can reduce annotation costs in surgical video analysis while maintaining model performance. However, traditional AL methods, developed for images or short vide…
F-ViTA: Foundation Model Guided Visible to Thermal Translation
Jay N. Paranjape, Celso de Melo, Vishal M. Patel
Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due…
RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation
Nuren Zhaksylyk, Ibrahim Almakky, Jay Paranjape +4
Accurate surgical instrument segmentation is essential in cataract surgery for tasks such as skill assessment and workflow optimization. However, limited annotated data makes it di…
:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training
Nisarg A. Shah, Wele Gedara Chaminda Bandara, Shameema Skider +2
Automated analysis of surgical videos is crucial for improving surgical training, workflow optimization, and postoperative assessment. We introduce a CSMAE, Masked Autoencoder (MAE…
Federated Black-Box Adaptation for Semantic Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
Federated Learning (FL) is a form of distributed learning that allows multiple institutions or clients to collaboratively learn a global model to solve a task. This allows the mode…