# Interactive Whole Slide Images for RL-based Tumour Segmentation

arXiv: https://arxiv.org/abs/2608.16607 · HTML: https://mlsanity.com/paper/2608.16607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Contrastive Learning as Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2206.07568/) (2022)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity](https://mlsanity.com/paper/1902.05605/) (2019)
- [Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1911.10334/) (2019)
- [Multiple Landmark Detection using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1907.00318/) (2019)
- [Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring](https://mlsanity.com/paper/1903.10762/) (2019)
- [Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet](https://mlsanity.com/paper/2006.14702/) (2020)
- [Deep Reinforcement Learning for Organ Localization in CT](https://mlsanity.com/paper/2005.04974/) (2020)
- [Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification](https://mlsanity.com/paper/1902.10946/) (2019)
- [Sequential Attention-based Sampling for Histopathological Analysis](https://mlsanity.com/paper/2507.05077/) (2025)
- [Mode-Dependent Rectification for Stable PPO Training](https://mlsanity.com/paper/2602.05619/) (2026)

## Abstract (arXiv)

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