Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …
learns a sequence of actions that maximizes the expected reward, with the representative …
Artificial intelligence for cognitive health assessment: state-of-the-art, open challenges and future directions
AR Javed, A Saadia, H Mughal, TR Gadekallu… - Cognitive …, 2023 - Springer
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led
many researchers to explore ways to automate the process to make it more objective and to …
many researchers to explore ways to automate the process to make it more objective and to …
Machine learning empowering personalized medicine: A comprehensive review of medical image analysis methods
Artificial intelligence (AI) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …
improved medical image processing and analysis in various tasks such as disease …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
A web-based automated image processing research platform for cochlear implantation-related studies
The robust delineation of the cochlea and its inner structures combined with the detection of
the electrode of a cochlear implant within these structures is essential for envisaging a safer …
the electrode of a cochlear implant within these structures is essential for envisaging a safer …
Adaptive scatter kernel deconvolution modeling for cone‐beam CT scatter correction via deep reinforcement learning
Z Piao, W Deng, S Huang, G Lin, P Qin, X Li… - Medical …, 2024 - Wiley Online Library
Background Scattering photons can seriously contaminate cone‐beam CT (CBCT) image
quality with severe artifacts and substantial degradation of CT value accuracy, which is a …
quality with severe artifacts and substantial degradation of CT value accuracy, which is a …
[HTML][HTML] Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images
Detection of abnormalities within the inner ear is a challenging task even for experienced
clinicians. In this study, we propose an automated method for automatic abnormality …
clinicians. In this study, we propose an automated method for automatic abnormality …
Deep reinforcement learning with explicit spatio-sequential encoding network for coronary ostia identification in CT images
Accurate identification of the coronary ostia from 3D coronary computed tomography
angiography (CCTA) is a essential prerequisite step for automatically tracking and …
angiography (CCTA) is a essential prerequisite step for automatically tracking and …
Deep reinforcement learning for detection of inner ear abnormal anatomy in computed tomography
Detection of abnormalities within the inner ear is a challenging task that, if automated, could
provide support for the diagnosis and clinical management of various otological disorders …
provide support for the diagnosis and clinical management of various otological disorders …
Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding
Medical images are generally acquired with limited field-of-view (FOV), which could lead to
incomplete regions of interest (ROI), and thus impose a great challenge on medical image …
incomplete regions of interest (ROI), and thus impose a great challenge on medical image …