Deep Reinforcement Learning for Medical Applications on Embedded Devices

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

1. Dr. Francesco Leporati
   Associate Professor of Computer Science
   University of Pavia, Italy
   Email: leporati@unipv.it
   Web: http://mclab.unipv.it/index.php/staff/2-non-categorizzato/17-leporati-eng
2. Dr. Emanuele Torti
   Assistant Professor of Electrical, Computer and Biomedical Engineering
   University of Pavia, Italy
   Email: emanuele.torti@unipv.it
   Web: https://scholar.google.com/citations?user=80A5s4wAAAAJ&hl=en
3. Dr. Yu-Dong Zhang
   Professor and Chair in Knowledge Discovery and Machine Learning
   University of Leicester, UK
   Email: yudong.zhang@le.ac.uk
   Web: https://www2.le.ac.uk/departments/informatics/people/yudong-zhang
4. Dr. Hong Lin
   Professor of Computer Science and Engineering Technology
   University of Houston Downtown, USA
   Email: linh@uhd.edu
   Web: https://www.uhd.edu/academics/sciences/computer-science-engineering-technology/Pages/bio-linh.aspx
5. Dr. João Manuel R. S. Tavares
   Associate Professor of Faculty of Engineering
   University of Porto, Portugal
   Email: tavares@fe.up.pt
   Web: https://web.fe.up.pt/~tavares/index_uk.html
6. Dr. Steven L. Fernandes (Managing Guest Editor)
   Assistant Professor of Computer Science
   Creighton University, USA
   Email: stevenfernandes@creighton.edu
   Web: http://www.creighton.edu/ccas/computersciencedesignjournalism/faculty/stevenfernandes