发表机构
Université Côte d’Azur; Inria; CNRS; INSERM; IBV; Politecnico di Torino; Bichat Hospital; Assistance Publique–Hôpitaux de Paris（蔚蓝海岸大学; 法国国家信息与自动化研究所; 法国国家科学研究中心; 法国国家健康与医学研究院; 生物信息研究所; 都灵理工大学; 比沙医院; 巴黎公共医疗集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对全切片图像分析的计算挑战，提出端到端强化学习框架，将WSI建模为交互式分层多分辨率环境，经PPO训练的智能体可直接完成全切片肿瘤分割，推理速度快且效果与基于图像块的方法相当，为计算病理学提供新方向。
AI 中文摘要
全切片图像（WSI）分析因切片空间分辨率极高且肿瘤区域分布稀疏，仍存在计算挑战。我们提出一种端到端强化学习框架，用于直接在WSI上进行序贯肿瘤分割。我们未将切片视为预定义的候选图像块集合，而是将WSI本身构建为分层多分辨率环境，智能体通过移动、缩放和肿瘤选择动作在该环境中导航。智能体在近端策略优化（PPO）训练的演员-评论家架构中，联合处理局部观测和全局缩略图表示。对肺腺癌WSI的实验表明，在全切片上直接进行序贯肿瘤分割是可行的，其粗分割质量与在相似放大倍数下运行的基于图像块的方法相当，同时将每片的推理时间缩短至几秒。我们进一步分析了环境设计和动作空间粒度的影响，结果表明，将WSI建模为交互式环境为基于强化学习的计算病理学提供了有前景的方向。
英文摘要
Whole-slide image (WSI) analysis remains computationally challenging due to the extremely large spatial resolution of slides and the sparse distribution of tumour regions. We propose an end-to-end reinforcement learning framework for sequential tumour segmentation directly on WSIs. Instead of treating the slide as a predefined collection of candidate patches, we formulate the WSI itself as a hierarchical multi-resolution environment through which an agent navigates using movement, zooming, and tumour selection actions. The agent jointly processes local observations and a global thumbnail representation within an actor-critic architecture trained using proximal policy optimization (PPO). Experiments on pulmonary adenocarcinoma WSIs demonstrate the feasibility of direct sequential tumour segmentation on full slides, achieving comparable coarse segmentation quality relative to patch-based approaches operating at similar magnification levels, while reducing inference time to a few seconds per slide. We further analyse the impact of environment design and action-space granularity. Our results suggest that modelling WSIs as interactive environments provides a promising direction for RL-based computational pathology
