Abstract
Surgical tool detection in computer-assisted intervention system aims to provide surgeons with specific supportive information. Existing supervised methods heavily rely on the volume of labeled data. However, manually annotating location of tools in surgical videos is quite time-consuming. To overcome this problem, we propose a semi-supervised pipeline for surgical tool detection, using strategies of highly confident pseudo labeling and strong augmentation driven consistency. To evaluate the proposed pipeline, we introduce a surgical tool detection dataset, Cataract Dataset for Tool Detection (CaDTD). Compared to the supervised baseline, our semi-supervised method improves mean average precision (mAP) by 4.3%. In addition, an ablative study was conducted to validate the effectiveness of the two strategies in our tool detection pipeline, and the results show the mAP improvement of 1.9% and 3.9%, respectively. The proposed dataset, CaDTD, is publicly available at https://github.com/evangel-jiang/CaDTD.
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Acknowledgments
This work was supported in part by the Guangdong Key Area Research and Development Program (2020B010165004), the Shenzhen Key Basic Science Program (JCYJ20180507182437217), the National Key Research and Development Program (2019YFC0118100 and 2017YFC0110903), the National Natural Science Foundation of China (12026602), and the Shenzhen Key Laboratory Program (ZDSYS201707271637577).
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Jiang, W., Xia, T., Wang, Z., Jia, F. (2021). Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_14
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