UPenn Team Speeds Up Surgical Video Annotation by 50% with Active Learning

A UPenn team published in IJCARS an active learning plus weak supervision framework that speeds up surgical video annotation by up to 50%, addressing the annotation bottleneck in surgical AI. The full paper is now online at Springer.

2026-11-11 ~ 2026-11-11 · 3 related posts

• Active learning with weak supervision cuts surgical video annotation effort by ~50% — Laparoscopes · 2026-11-11
• Full paper: active learning cuts surgical video annotation effort by ~50% — santiviquez · 2026-11-11

1 near-duplicate retellings: Laparoscopes