ArticleBMC medical imaging2022
Reinforcement learning using Deep
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Semi-supervised Label Generation for 3D Multi-modal MRI Bone Tumor Segmentation.Journal of imaging informatics in medicine · 2025Article
- The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence.Scientific reports · 2025Article
- Review
- Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Advanced CNN Architecture for Brain Tumor Segmentation and Classification using BraTS-GOAT 2024 Dataset.Current medical imaging · 2025Article
- Enhancing Medical Training Through Learning From Mistakes by Interacting With an Ill-Trained Reinforcement Learning Agent.IEEE transactions on learning technologies · 2024Article
- Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges.Cureus · 2023Review
- Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes.Journal of digital imaging · 2022Article
- Convolutional neural networks: applications, challenges and future prospects in brain tumor research.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundSupervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition. It has recently been proposed that reinforcement learning addresses all three of these limitations. Notable prior work applied deep reinforcement learning to localize brain tumors with radiologist eye tracking points, which limits the state-action space. Here, we generalize Deep Q Learning to a gridworld-based environment so that only the images and image masks are required.
methodsWe trained a Deep
resultsWhereas the supervised approach quickly overfit the training data and predictably performed poorly on the testing set (11% accuracy), the Deep
conclusionWe have successfully applied reinforcement learning to localize brain tumors on 2D contrast-enhanced MRI brain images. This represents a generalization of recent work to a gridworld setting naturally suitable for analyzing medical images. We have shown that reinforcement learning does not over-fit small training sets, and can generalize to a separate testing set.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.