SEDCLIP: Adapting vision-language model for multi-label surgical error detection.
Automated detection of surgical errors can drastically increase safety in robotic-assisted surgery (RAS). Prior work has shown surgical error detection only as a binary task, which is insufficient and restrictive when multiple, co-occurring errors inherently arise in real RAS procedures. We thus present the first work...