Deep Reinforcement Learning for Medical Applications on Embedded Devices
in Special Issue Posted on December 10, 2020Information for the Special Issue
Submission Deadline: | Wed 30 Jun 2021 |
Journal Impact Factor : | 1.161 |
Journal Name : | Microprocessors and Microsystems |
Journal Publisher: |
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Website for the Special Issue: | https://www.journals.elsevier.com/microprocessors-and-microsystems/call-for-papers/deep-reinforcement-learning-for-medical-applications |
Journal & Submission Website: | https://www.journals.elsevier.com/microprocessors-and-microsystems |
Special Issue Call for Papers:
Deep reinforcement learning (DRL) uses feedback from the agent to make decisions in complex problems under uncertainty. Medical applications often require processing large volumes of complex data in a challenging environment. Deep reinforcement learning can process this data by analyzing the agent’s feedback that is sequential and sampled using non-linear functions. The deep reinforcement learning algorithms commonly used for medical applications include value-based methods, policy gradient, and actor-critic methods. The recent advances in the increased computational capabilities of architectures like field-programmable gate array (FPGA), graphics processing units (GPU), and digital signal processors (DSP) have made it possible to infer deep reinforcement learning algorithms on them. However, efficient implementation of these architectures should consider the issues related to their portability, wearability, and power consumption.
The main objective is to provide a platform for scientists, researchers, industry experts, and scholars to share their innovative contributions in deep reinforcement learning for medical applications on various embedded devices (ED). Research articles describing only a proof of concept are not encouraged. Authors are solicited to develop novel deep reinforcement learning algorithms on medical data and implement them either on FPGA, GPUs, or DSP. The special issue invites authors to submit papers that analyze the portability, wearability, power consumption of the deep reinforcement learning algorithms implemented either on FPGA, GPU, or DSP. The deep reinforcement learning topic includes but not restricted to:
- Monte Carlo Tree Search and Deep Q-network
- Dual Gradient Descent and Conjugate Gradient
- Trust Region Policy Optimization and Proximal Policy Optimization.
- Actor-Critic using Kronecker-Factored Trust Region
- Linear Quadratic Regulator and Iterative Linear Quadratic Regulator
- Twin-Delayed Deep deterministic policy gradient
- Guided Policy Search and Model-Based Learning with Raw Medical Videos
- Inverse Reinforcement Learning and Meta-learning
- Very efficient ED for DRL in medical applications in terms of power consumption, processing efficiency and flexibility
- Neuromorphic and/or brain-inspired architectures implementing DRL techniques
- Efficient mapping of DRL applications to ED
- New learning approaches for DRL targeting ED
- High-level programming language support, tools, frameworks, and system software for DRL in medical applications implemented on ED
- Security and Reliability issues for DRL on ED
- DRL ED implementation in cyber-physical systems for healthcare, well-being and personal assistance (elderly, disability), sports and medicine, rehabilitation, instrumentation, lab-on-chips
Important dates
- Paper submission due: June 30, 2021
- First-round acceptance notification: August 30, 2021
- Revision submission: October 15, 2021
- Notification of final decision: December 30, 2021
- Submission of final paper: January 30, 2022
- Publication date: March 2022
Submission instructions
When submitting your manuscript please select the article type “VSI:DRL-MA-ED”. Please submit your manuscript before the submission deadline. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Guest Editors
-
Dr. Francesco Leporati
Associate Professor of Computer Science
University of Pavia, Italy
Email: [email protected]
Web: http://mclab.unipv.it/index.php/staff/2-non-categorizzato/17-leporati-eng -
Dr. Emanuele Torti
Assistant Professor of Electrical, Computer and Biomedical Engineering
University of Pavia, Italy
Email: [email protected]
Web: https://scholar.google.com/citations?user=80A5s4wAAAAJ&hl=en -
Dr. Yu-Dong Zhang
Professor and Chair in Knowledge Discovery and Machine Learning
University of Leicester, UK
Email: [email protected]
Web: https://www2.le.ac.uk/departments/informatics/people/yudong-zhang -
Dr. Hong Lin
Professor of Computer Science and Engineering Technology
University of Houston Downtown, USA
Email: [email protected]
Web: https://www.uhd.edu/academics/sciences/computer-science-engineering-technology/Pages/bio-linh.aspx -
Dr. João Manuel R. S. Tavares
Associate Professor of Faculty of Engineering
University of Porto, Portugal
Email: [email protected]
Web: https://web.fe.up.pt/~tavares/index_uk.html -
Dr. Steven L. Fernandes (Managing Guest Editor)
Assistant Professor of Computer Science
Creighton University, USA
Email: [email protected]
Web: http://www.creighton.edu/ccas/computersciencedesignjournalism/faculty/stevenfernandes
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