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HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

Zhi-Bo Liu, Xiaobo Pang, Jizhao Wang, Shuai Liu, Chen Li

TL;DR

HistoGym reframes histopathological image analysis as a sequential, multi-scale decision problem and introduces an open-source RL environment built on the OpenSlide pyramid to simulate a pathologist's diagnostic workflow. By integrating with Farama Gymnasium and providing configurable observations, actions, and rewards, it supports tumor detection and classification tasks across multiple organs and scenarios. Baseline experiments with PPO and Ape-X DQN reveal that environmental complexity and representation choices strongly influence learning, highlighting challenges in continuous action spaces and pixel-level policy learning. The platform offers a standardized, extensible benchmark to accelerate RL research in computational pathology and to improve interpretability through traceable diagnostic trajectories.

Abstract

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.

HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

TL;DR

HistoGym reframes histopathological image analysis as a sequential, multi-scale decision problem and introduces an open-source RL environment built on the OpenSlide pyramid to simulate a pathologist's diagnostic workflow. By integrating with Farama Gymnasium and providing configurable observations, actions, and rewards, it supports tumor detection and classification tasks across multiple organs and scenarios. Baseline experiments with PPO and Ape-X DQN reveal that environmental complexity and representation choices strongly influence learning, highlighting challenges in continuous action spaces and pixel-level policy learning. The platform offers a standardized, extensible benchmark to accelerate RL research in computational pathology and to improve interpretability through traceable diagnostic trajectories.

Abstract

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.
Paper Structure (25 sections, 3 equations, 2 figures, 2 tables)

This paper contains 25 sections, 3 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivation and Related Work
  3. Mimicking and Explainability
  4. Computational Efficiency
  5. Reinforcement Learning Environments in the Medical Domain
  6. Problem Formulation: From Classification to Reinforcement Learning
  7. Supervised Learning
  8. Multi-instance Learning
  9. Reinforcement Learning for WSI Analysis
  10. Transition to RL
  11. HistoGym Environment
  12. The RL Environment Class
  13. HistoGym Environment Class
  14. RL Framework
  15. Environment Complexity & Scenario of Different Levels
  16. ...and 10 more sections

Figures (2)

  • Figure 1: An illustration of the HistoGym Environment. This figure illustrates the core components of the HistoGym environment, including the agent’s interaction with the whole slide image (WSI) through a series of actions. The environment leverages tile-based processing and multi-scale navigation, allowing the agent to effectively analyze high-resolution histopathology images.
  • Figure 2: HistoGym Environment Complexity