Active Learning Halves Surgical Video Annotation Effort with Human-in-the-Loop Framework

Jul 16, 2026

A new human-in-the-loop framework combines active learning and weak supervision to reduce the annotation effort required for surgical video segmentation by 50%. The approach leverages a foundation model to generate temporally consistent class activation maps and iteratively refines pseudo-masks with minimal expert input. This method eliminates the need for large, fully annotated datasets at the outset, enabling more scalable development of surgical tool segmentation models.

Why it matters: Reducing annotation effort makes it more feasible to develop and deploy surgical video analysis models in real-world clinical settings.

Full story at: arXiv Computer Vision ↗