International journal of computer assisted radiology and surgery · 2026
CVS assessment via distillation-based self-supervised and multiple instance learning in laparoscopic cholecystectomy.
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Wang Hao, Zhang Yutao, Yang Yuxuan, Zhu Yuanbo, Xu Rui
Аннотация
PURPOSE: Accurate and automated assessment of the critical view of safety (CVS) is crucial for preventing bile duct injuries during laparoscopic cholecystectomy (LC). Existing methods often rely on costly segmentation labels or sequential inputs, limiting generalization and spatiotemporal understanding. This study proposes an efficient framework that removes the need for segmentation annotations while enhancing model robustness and temporal-spatial comprehension.
METHODS: We introduce SMIL framework, a novel framework for automated CVS assessment that combines distillation-based self-supervised pretraining and multiple instance learning. A video transformer is first pretrained using label-free self-distillation to capture rich spatiotemporal features. We conducted a benchmark evaluation on the public Endoscapes2023 dataset, comprising 201 LC videos whose CVS-relevant frames are released at 1 fps (58,813 frames in total). Training/validation/testing followed the official video-level split of 120/41/40 videos. It is then fine-tuned via MIL by fusing global and local representations for multi-label CVS classification.
RESULTS: Experimental results on the official test partition show that SMIL framework outperforms state-of-the-art methods without relying on segmentation labels. Compared to the strongest label-free baseline, SMIL achieves gains of 3.21% in mean average precision and 2.74% in balanced accuracy, setting a new benchmark for automated CVS assessment without dense annotations. Notably, SMIL also surpasses segmentation-supervised models in mAP, further highlighting its efficient learning capability.
CONCLUSION: The SMIL framework enables automated CVS assessment without segmentation annotations or sequential inputs. By combining self-supervised and multiple instance learning, it enhances spatiotemporal understanding and generalization in LC surgeries, offering both theoretical insights and practical value for surgical safety.
METHODS: We introduce SMIL framework, a novel framework for automated CVS assessment that combines distillation-based self-supervised pretraining and multiple instance learning. A video transformer is first pretrained using label-free self-distillation to capture rich spatiotemporal features. We conducted a benchmark evaluation on the public Endoscapes2023 dataset, comprising 201 LC videos whose CVS-relevant frames are released at 1 fps (58,813 frames in total). Training/validation/testing followed the official video-level split of 120/41/40 videos. It is then fine-tuned via MIL by fusing global and local representations for multi-label CVS classification.
RESULTS: Experimental results on the official test partition show that SMIL framework outperforms state-of-the-art methods without relying on segmentation labels. Compared to the strongest label-free baseline, SMIL achieves gains of 3.21% in mean average precision and 2.74% in balanced accuracy, setting a new benchmark for automated CVS assessment without dense annotations. Notably, SMIL also surpasses segmentation-supervised models in mAP, further highlighting its efficient learning capability.
CONCLUSION: The SMIL framework enables automated CVS assessment without segmentation annotations or sequential inputs. By combining self-supervised and multiple instance learning, it enhances spatiotemporal understanding and generalization in LC surgeries, offering both theoretical insights and practical value for surgical safety.