mlsanityData to 7 Oct 2026

RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning

arXiv 2212.01737 ↗First posted 4 Dec 2022cs.CV cs.AI cs.LGcited by 8

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Abstract (arXiv)

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: urlhttps://github.com/tencent-ailab/RLogist.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 4 Dec 2022.

Builds on

31 of 53 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2017Proximal Policy Optimization Algorithmscited by 14,441
  5. 2016OpenAI Gymcited by 1,684
  6. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  7. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  8. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  9. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  10. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  11. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  12. 2017Deep Reinforcement Learning: An Overviewcited by 327
19 more
  1. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 163
  2. 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 150
  3. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  4. 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
  5. 2020Active MR k-space Sampling with Reinforcement Learningcited by 45
  6. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  7. 2021JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learningcited by 28
  8. 2018Deep Reinforcement Learning for Surgical Gesture Segmentation and Classificationcited by 15
  9. 2019Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoringcited by 14
  10. 2019PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processingcited by 13
  11. 2020Synthetic Sample Selection via Reinforcement Learningcited by 13
  12. 2020Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNetcited by 11
  13. 2020Auto-weighting for Breast Cancer Classification in Multimodal Ultrasoundcited by 9
  14. 2020Reinforcement Learning of Musculoskeletal Control from Functional Simulationscited by 8
  15. 2017Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learningcited by 7
  16. 2017Group-driven Reinforcement Learning for Personalized mHealth Interventioncited by 6
  17. 2019Adversarial Policy Gradient for Deep Learning Image Augmentationcited by 4
  18. 2020Augmenting Policy Learning with Routines Discovered from a Single Demonstrationcited by 4
  19. 2018Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classificationcited by 12
  2. 2024Affordable Generative Agentscited by 8
  3. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  4. 2025Sequential Attention-based Sampling for Histopathological Analysiscited by 2
  5. 2024HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysiscited by 1
  6. 2025The Impact of Image Resolution on Biomedical Multimodal Large Language Modelscited by 1
  7. 2026Agentic Visual Reasoning in Whole-Slide Pathology Images via Active Perceptioncited by 1
  8. 2026PAVE: A Cognitive Architecture for Legitimate Violation in Generative Agent Societiescited by 0