Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes

Stember, Joseph N.; Shalu, Hrithwik

doi:10.1007/s10278-022-00644-5

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…To overcome this limitation, Stember & Shalu published two papers 30,55 which used a DQN agent in tandem with a TD model for accurate image classification with a minimal training set. The main workflows of these two papers were identical, except that the labels in the second paper were extracted from clinical reports using an SBERT 56 .…”

Section: Rl In Medical Image Analysismentioning

confidence: 99%

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

Zhang

Luke

et al. 2023

J Applied Clin Med Phys

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Motivation Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches’ limitations and possible future improvements.

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Section: Rl In Medical Image Analysismentioning

confidence: 99%

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

Zhang

Luke

et al. 2023

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…However, this is not true because for a large B, the worst value (lower end of the confidence interval) of the mean estimate for τ = 1 is considerably lower than the worst value for τ = 4. Moreover, choosing a large B may not always be feasible due to memory constraints as it requires loading more samples at each step (Stember & Shalu, 2021). Increasing B also requires more computation per step.…”

Section: Hyperparameter Sensitivity Analysismentioning

confidence: 99%

A Novel Higher Level Symmetrical and Asymmetrical Multilevel Inverter for Solar Energy Environment

Kumar

Raj

Nema

et al. 2020

IETE Journal of Research

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Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample efficiency. However, learning accurate estimates of the model is hard. Subsequently, the natural question is whether we can get similar benefits as planning with modelfree methods. Experience replay is an essential component of many model-free algorithms enabling sample-efficient learning and stability by providing a mechanism to store past experiences for further reuse in the gradient computational process. Prior works have established connections between models and experience replay by planning with the latter. This involves increasing the number of times a mini-batch is sampled and used for updates at each step (amount of replay per step). We attempt to exploit this connection by doing a systematic study on the effect of varying amounts of replay per step in a well-known modelfree algorithm: Deep Q-Network (DQN) in the Mountain Car environment. We empirically show that increasing replay improves DQN's sample efficiency, reduces the variation in its performance, and makes it more robust to change in hyperparameters. Altogether, this takes a step toward a better algorithm for deployment.

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Deep Neuroevolution Squeezes More Out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification

Stember¹,

Shalu²

2023

International Symposium on Intelligent Informatics

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Deep Reinforcement Learning with Automated Label Extraction from Clinical Reports Accurately Classifies 3D MRI Brain Volumes

Cited by 9 publications

References 22 publications

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

A Novel Higher Level Symmetrical and Asymmetrical Multilevel Inverter for Solar Energy Environment

Deep Neuroevolution Squeezes More Out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification

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