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Being able to automatically detect and track surgical instruments inendoscopic video recordings would allow for many useful applications that couldtransform different aspects of surgery. In robot-assisted surgery, thepotentially informative data like categories of surgical tool can be captured,which is sparse, full of noise and without spatial information. We proposed aWeakly Supervised Yolo Network (WS-YOLO) for Surgical Tool Localization inEndoscopic Videos, to generate fine-grained semantic information with locationand category from coarse-grained semantic information outputted by the da Vincisurgical robot, which significantly diminished the necessary human annotationlabor while striking an optimal balance between the quantity of manuallyannotated data and detection performance. The source code is available athttps://github.com/Breezewrf/Weakly-Supervised-Yolov8.

WS-YOLO: Weakly Supervised Yolo Network for Surgical Tool Localization in Endoscopic Videos