论文提出了 DiffeoAfford，不用手工逐帧标注就能学会预测手术关键区域，带动了自动取景系统，实测医生认知负荷更低。
DiffeoAfford 框架从已完成手术中回顾性生成视觉注意力监督，无需人工逐帧标注。它结合微分同胚约束的组织追踪与器械轨迹分析，生成 affordance 热点标签。基于这些标签训练的实时预测模型驱动 AffordView 自动取景系统。系统与专家标注和术中外科医生凝视一致，实测降低认知负荷。
Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery
Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to obtain because surgical intent is highly specialized and tacit. Here, we introduce DiffeoAfford, an action-grounded tissue affordance framework that retrospectively derives visual attention supervision from completed surgical procedures. By combining diffeomorphism-constrained tissue tracking with instrument trajectory analysis, DiffeoAfford generates affordance hotspot labels without manual per-frame annotation. A real-time prediction model trained on these labels anticipates relevant surgical regions and enables AffordView, an assistive auto-framing system for laparoscopic visualization. The proposed framework aligns with expert annotations and intraoperative surgeon gaze, and reduces surgeon cognitive workload during real-world evaluations using subjective, physiological, and behavioral measures.