Abstract
Purpose
Surgical action triplet recognition is a clinically significant yet challenging task. It provides surgeons with detailed information about surgical scenarios, thereby facilitating clinical decision-making. However, the high similarity among action triplets presents a formidable obstacle to recognition. To enhance accuracy, prior methods necessitated the utilization of larger models, thereby incurring a considerable computational burden.
Methods
We propose a novel framework known as the Lite and Mega Models (LAM). It comprises a CNN-based fully fine-tuned model (LAM-Lite) and a parameter-efficient fine-tuned model based on the foundation model using Transformer architecture (LAM-Mega). Temporal multi-label data augmentation is introduced for extracting robust class-level features.
Results
Our study demonstrates that LAM outperforms prior methods across various parameter scales on the CholecT50 dataset. Using fewer tunable parameters, LAM achieves a mean average precision (mAP) of 42.1%, a 3.6% improvement over the previous state of the art.
Conclusion
Leveraging effective structural design and robust capabilities of the foundational model, our proposed approach successfully strikes a balance between accuracy and computational efficiency. The source code is accessible at https://github.com/Lycus99/LAM.
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Funding
The present study was supported in part by the Shenzhen Science and Technology Program (SGDX20230116092200001), the National Natural Science Foundation of China (Nos. 62172401 and 82227806), the Guangdong Natural Science Foundation (Nos. 2022A1515010439 and 2022A0505020019), the Shenzhen Key Basic Research Grant (No. JCYJ20220818101802005), the Zhuhai Science and Technology Program (No. ZH22017002210017PWC), and the Education Science Planning Program of Guangdong Department of Education (Higher Education Special Project) (No. 2022GXJK325).
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Li, Y., Bai, B. & Jia, F. Parameter-efficient framework for surgical action triplet recognition. Int J CARS 19, 1291–1299 (2024). https://doi.org/10.1007/s11548-024-03147-6
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DOI: https://doi.org/10.1007/s11548-024-03147-6