International journal of computer assisted radiology and surgery · 2026
Surg-NAT+: negation-aware vision-language refinement for fine-grained surgical understanding.
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Yuan Kun, Cao Yutong, Tang Yunxi, Padoy Nicolas, Chu Xiangyu
Аннотация
PURPOSE: Surgical vision-language foundation models learn generalizable representations from large-scale surgical data and support flexible adaptation to diverse downstream tasks by using text prompts as classifiers. However, their effectiveness is hindered by a critical semantic limitation: These models often struggle to distinguish between positive and negative textual assertions, a capability essential for fine-grained surgical tasks where the target object may occupy only a small and localized region. This limitation weakens the reliability of current state-of-the-art vision-language adaptation methods, leading to degraded performance when prompts require precise semantic discrimination.
METHODS: We propose Surg-NAT+, a few-shot vision-language adaptation framework designed to enhance foundation models for fine-grained surgical tasks. First, we introduce a negation-aware contrastive objective that explicitly strengthens the model's ability to differentiate between affirmative and negated textual prompts. This objective is incorporated into pretrained models through multi-level adapter fusion (MAF), enabling hierarchical semantic refinement within the text encoder. Second, we propose a fine-grained self-distillation objective that improves visual grounding by enforcing consistency between global representations of image crops and the corresponding local patch embeddings.
RESULTS: We evaluate our approach on the Cholec80 dataset for few-shot surgical tool recognition using foundation models. Surg-NAT+ achieves state-of-the-art performance across all few-shot regimes, consistently surpassing existing baselines by a substantial margin. Qualitative analyses further demonstrate that our method yields more accurate visual grounding of target tools and enhances semantic separability in the learned feature space.
CONCLUSIONS: We presented Surg-NAT+, a lightweight negation-aware adaptation framework that substantially improves the fine-grained recognition capabilities of surgical vision-language models in few-shot, multi-label tool recognition settings. Our approach provides an efficient and semantically robust pathway for adapting foundation models to surgical domains. The code is available at https://github.com/Yutongc-ai/fine-tune-SurgVLP .
METHODS: We propose Surg-NAT+, a few-shot vision-language adaptation framework designed to enhance foundation models for fine-grained surgical tasks. First, we introduce a negation-aware contrastive objective that explicitly strengthens the model's ability to differentiate between affirmative and negated textual prompts. This objective is incorporated into pretrained models through multi-level adapter fusion (MAF), enabling hierarchical semantic refinement within the text encoder. Second, we propose a fine-grained self-distillation objective that improves visual grounding by enforcing consistency between global representations of image crops and the corresponding local patch embeddings.
RESULTS: We evaluate our approach on the Cholec80 dataset for few-shot surgical tool recognition using foundation models. Surg-NAT+ achieves state-of-the-art performance across all few-shot regimes, consistently surpassing existing baselines by a substantial margin. Qualitative analyses further demonstrate that our method yields more accurate visual grounding of target tools and enhances semantic separability in the learned feature space.
CONCLUSIONS: We presented Surg-NAT+, a lightweight negation-aware adaptation framework that substantially improves the fine-grained recognition capabilities of surgical vision-language models in few-shot, multi-label tool recognition settings. Our approach provides an efficient and semantically robust pathway for adapting foundation models to surgical domains. The code is available at https://github.com/Yutongc-ai/fine-tune-SurgVLP .