HistoGym：面向组织病理学图像分析的强化学习环境
HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis
AI总结：
针对传统patch方法难以建模全切片病理图像多尺度结构的问题，研究团队推出遵循OpenAI Gym API的开源强化学习环境HistoGym，模拟医生诊断流程，支持多器官多癌症场景的病理分析任务。
摘要：
在病理学研究、教学与临床实践中，基于病理图像的决策流程至关重要。这一重要性同样体现在数字病理图像分析领域：组织结构中蕴含的海量信息可充分证明其价值，这对精准的癌症分类与分级不可或缺；此外，算法生成的结论天然需要可解释性，也凸显了该领域的必要性。对人类而言，判定肿瘤类型与分级通常需要多尺度分析，这对AI算法构成了重大挑战。传统的基于图像块（patch）的方法无法对这类复杂结构建模，因为它们难以捕捉全切片图像（whole slide image，WSI）中固有的复杂多尺度信息。因此，亟需能够高效、精准复现这一复杂分析流程的先进AI技术。为解决这一问题，我们推出了HistoGym——一款面向组织病理学图像分析的开源强化学习环境。HistoGym遵循OpenAI Gym的应用程序接口（API）规范，通过模拟医生的实际诊断流程，旨在推动全切片图像诊断的发展。依托WSI的金字塔特征与OpenSlide API，HistoGym为肿瘤检测、分类等多种临床任务提供了统一框架。我们详细阐述了专为组织病理学图像分析领域定制的观测空间、动作空间与奖励函数设计，并为临床医生与研究人员提供了基于Python的开源接口。为适配不同临床需求，我们提供了针对不同器官与癌症的多种场景，包括基于全切片图像的场景与基于选定区域的场景，并展示了若干值得关注的结果。
英文摘要：
In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important. This significance extends to digital pathology image analysis: its adequacy is demonstrated by the extensive information contained within tissue structures, which is essential for accurate cancer classification and grading. Additionally, its necessity is highlighted by the inherent requirement for interpretability in the conclusions generated by algorithms. For humans, determining tumor type and grade typically involves multi-scale analysis, which presents a significant challenge for AI algorithms. Traditional patch-based methods are inadequate for modeling such complex structures, as they fail to capture the intricate, multi-scale information inherent in whole slide images. Consequently, there is a pressing need for advanced AI techniques capable of efficiently and accurately replicating this complex analytical process. To address this issue, we introduce HistoGym, an open-source reinforcement learning environment for histopathological image analysis. Following OpenAI Gym APIs, HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors. Leveraging the pyramid feature of WSIs and the OpenSlide API, HistoGym provides a unified framework for various clinical tasks, including tumor detection and classification. We detail the observation, action, and reward specifications tailored for the histopathological image analysis domain and provide an open-source Python-based interface for both clinicians and researchers. To accommodate different clinical demands, we offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios, showcasing several noteworthy results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
