Abstract
Surgical phase recognition is a critical component for context-aware decision support in intelligent operating rooms, yet training robust models is hindered by limited annotated clinical videos and large domain gaps between synthetic and real surgical data. To address this, we propose CauCLIP, a causality-inspired vision-language framework that leverages CLIP to learn domain-invariant representations for surgical phase recognition without access to target domain data. Our approach integrates a frequency-based augmentation strategy to perturb domain-specific attributes while preserving semantic structures, and a causal suppression loss that mitigates non-causal biases and reinforces causal surgical features. These components are combined in a unified training framework that enables the model to focus on stable causal factors underlying surgical workflows. Experiments on the SurgVisDom hard adaptation benchmark demonstrate that our method substantially outperforms all competing approaches, highlighting the effectiveness of causality-guided vision-language models for domain-generalizable surgical video understanding.
Abstract (translated)
手术阶段识别是智能手术室中上下文感知决策支持的关键组成部分，然而由于临床视频的标注数据有限以及合成和真实外科数据之间的领域差异较大，训练稳健模型面临挑战。为解决这些问题，我们提出了一种受因果关系启发的视觉-语言框架CauCLIP，该框架利用CLIP（对比语言-图像预训练）来学习无需访问目标领域数据即可进行手术阶段识别的领域不变表示。 我们的方法结合了一个基于频率的数据增强策略，可以扰动特定领域的属性同时保留语义结构，并引入了一种因果抑制损失函数以减轻非因果偏见并强化因果外科特征。这些组件被整合进一个统一的训练框架中，使模型能够专注于手术流程背后稳定的因果因素。 在SurgVisDom硬适应基准上的实验表明，我们的方法显著优于所有竞争的方法，这凸显了受因果关系指导的视觉-语言模型在跨领域手术视频理解中的有效性。
URL
https://arxiv.org/abs/2602.06619
