Weakly Supervised YOLO Network for Surgical Instrument Localization in Endoscopic Videos

• URL: http://arxiv.org/abs/2309.13404v3
• Date: Fri, 21 Jun 2024 02:18:57 GMT
• Title: Weakly Supervised YOLO Network for Surgical Instrument Localization in Endoscopic Videos
• Authors: Rongfeng Wei, Jinlin Wu, Xuexue Bai, Ming Feng, Zhen Lei, Hongbin Liu, Zhen Chen,
• Abstract summary: We propose a weakly supervised localization framework named WS-YOLO for surgical instruments. By leveraging the instrument category information as the weak supervision, our WS-YOLO framework adopts an unsupervised multi-round training strategy for the localization capability training. We validate our WS-YOLO framework on the Endoscopic Vision Challenge 2023 dataset, which achieves remarkable performance in the weakly supervised surgical instrument localization.
• Score: 17.304000735410145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In minimally invasive surgery, surgical instrument localization is a crucial task for endoscopic videos, which enables various applications for improving surgical outcomes. However, annotating the instrument localization in endoscopic videos is tedious and labor-intensive. In contrast, obtaining the category information is easy and efficient in real-world applications. To fully utilize the category information and address the localization problem, we propose a weakly supervised localization framework named WS-YOLO for surgical instruments. By leveraging the instrument category information as the weak supervision, our WS-YOLO framework adopts an unsupervised multi-round training strategy for the localization capability training. We validate our WS-YOLO framework on the Endoscopic Vision Challenge 2023 dataset, which achieves remarkable performance in the weakly supervised surgical instrument localization. The source code is available at https://github.com/Breezewrf/WS-YOLO.

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