Robust Automatic Multiple Landmark Detection

Jain, Arjit; Powers, Alexander; Johnson, Hans J.

doi:10.1109/isbi45749.2020.9098329

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…Hence, various approaches have been proposed. For instance, Nan et al enable distributed SADRL agents to use historical knowledge to ensure stability (O.1) in [69], and Arjit et al detects missing data for improved reliability of medical image analysis in [71]. The rest of this section presents the SADRL approaches applied to multi-agent environments.…”

Section: Sadrl Applied To Distributed Agents In a Multi-agent Environmentmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

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Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

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Section: Sadrl Applied To Distributed Agents In a Multi-agent Environmentmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

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“…The backbone CNN is shared across all agents while the policy-making fully connected layers are separate for each agent. Different from the previous works on RL-based landmark detection, which detect a single landmark, [161] proposed a multiple landmark detection approach to better time-efficient and more robust to missing data. In their approach, each landmark is guided by one agent.…”

Section: Drl In Landmark Detectionmentioning

confidence: 99%

“…11. The figure illustrates a general implementation of landmark detection with the help of DRL, where the state is the Region of interest (ROI) around the current landmark location cropped from the image, The actions performed by the DRL agent are responsible for shifting the ROI across the image forming a new state and the reward corresponds to the improvement in euclidean distance between ground truth and predicted landmark location with iterations as used by [105], [7], [10], [377], [161].…”

Section: Drl In Landmark Detectionmentioning

confidence: 99%

Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey

Le¹,

Rathour²,

Yamazaki³

et al. 2021

Preprint

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Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i) landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision.

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“…Another research direction is the detection of lesion areas and landmarks. Jain et al [18] proposed a multi landmark detection method to improve time efficiency and enhance robustness for missing data. Liu et al [19] proposed using DRL models for lung cancer detection and tested multiple deep reinforcement learning models, such as DQN.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Method for 3D-CT Nasopharyngeal Cancer Localization with Prior Knowledge

Han

Kong

et al. 2023

Applied Sciences

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Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.

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Robust Automatic Multiple Landmark Detection

Cited by 6 publications

References 8 publications

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey

Deep Reinforcement Learning Method for 3D-CT Nasopharyngeal Cancer Localization with Prior Knowledge

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