用强化学习直接在整张病理切片上逐块分割肿瘤，效率提升明显。
Interactive Whole Slide Images for RL-based Tumour Segmentation
- 将切片建模为可移动、缩放的交互环境，智能体自主探索并分割肿瘤。
- 在肺腺癌切片上实现每张仅需数秒的推理，分割效果接近传统分块方法。
- 适合需要高效病理分析的临床科研与自动化诊断场景。
全切片图像（WSI）分析因分辨率极高且肿瘤区域稀疏而计算复杂。本文提出一种端到端强化学习框架，直接在完整切片上进行序列化肿瘤分割。不将切片预设为候选图块集合，而是将其建模为多尺度交互环境，智能体通过移动、缩放和肿瘤选择动作导航其中。在演员-评论家架构中联合处理局部观测与全局缩略图表示，采用近端策略优化（PPO）训练。在肺腺癌WSI上的实验表明，该方法实现了全切片直接序列分割的可行性，在相似放大倍率下分割质量与分块方法相当，同时将推理时间缩短至每张数秒。进一步分析了环境设计与动作空间粒度的影响。结果表明，将WSI建模为交互环境为基于强化学习的计算病理学提供了有前景的方向。
原文摘要 · Abstract (English)
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
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