Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things

Submission Deadline: 31 October 2018

IEEE Access invites manuscript submissions in the area of Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things.

The recent advancement in Internet of Medical Things (IoMT) paradigm aims to enrich our perception of healthcare reality, and incorporating new technologies for such applications. In the context of the IoMT, several medical devices connected to healthcare IT infrastructure can offer superior and more personalized health services. The combination of IoMT data, machine learning, streaming analytics distributed computing, and biomedical systems has become more powerful by enabling the storage and analysis of more data and many different types of data much faster. Machine learning plays a crucial role in the medical imaging field, comprising computer-aided diagnosis, registration and fusion, image segmentation, image-guided therapy, and image database retrieval for providing a better understanding of medical data applied to biomedical systems in IoMT. Moreover, the potential of big data in IoMT is a critical concern to constructing and running the kinds of big data analytics applications are obligatory for IoMT data. Thus it necessitates key focus from academia and industries.

Medical data is central to the IoMT paradigm: from acquiring critical medical sensor data or imaging data to analyzing, processing, and storing of health information, which adds new insights to our view of the world. Machine learning is essential to challenges related to the data source applied to biomedical devices using IoMT. Machine learning and data-driven methods represent a paradigm shift, and they are bound to have a transformative impact in the area of medical data and imaging processing. Many challenges arise as the IoMT permeates our world, especially for low-power resource-constrained devices for accumulating patient’s data, medical data integrity, privacy and security, and network lifetime and quality of service among others. The primary goal of this Special Section in IEEE Access is to provide an overview of the current state-of-the-art advances in machine learning of data source for understanding IoMT.

Topics of interest include, but are not limited to:

  • Computer-aided detection or diagnosis applied to biomedical systems in IoMT
  • New imaging modalities or methodologies for IoMT
  • Innovative machine-learning algorithms or applications in IoMT
  • Medical data security and privacy techniques for healthcare
  • Energy harvesting and big data analytics strategies in IoMT
  • Deep learning for optimizing medical big data in IoMT
  • Low-power resource-constrained medical devices for IoMT
  • Associative rule learning and reinforcement learning in IoMT
  • Smart medical systems based on cloud-assisted body area networks
  • Flexible and wearable sensors for prognosis and follow-up based on IoMT Paradigm
  • Healthcare Informatics to analyze patient health records, for enabling better clinical decision making and improved healthcare outcomes

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Kelvin KL Wong, Western Sydney University, Australia

Guest Editors:

  1. Dhanjoo N Ghista, University 2020 Foundation, USA
  2. Giancarlo Fortino, University of Calabria (Unical), Italy
  3. Wanqing Wu, Chinese Academy of Sciences, China

 

Relevant IEEE Access Special Sections:

  1. Mobile Multimedia for Healthcare
  2. Health Informatics for the Developing World
  3. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: Kelvin.Wong@westernsydney.edu.au

Information Security Solutions for Telemedicine Applications

Submission Deadline: 31 March 2018

IEEE Access invites manuscript submissions in the area of Information Security Solutions for Telemedicine Applications.

In recent times, implementing telemedicine solutions has become a trend amongst the various research teams at an international level. Telemedicine refers to the use of modern information and communication technologies to meet the needs of citizens, patients, healthcare professionals, healthcare providers, as well as policy makers. Telemedicine applications are very promising and have great potential. They can play a very important role in service provision by improving access, equity and quality through connecting healthcare facilities and healthcare professionals and diminishing geographical and physical barriers. However, the transmission and access technologies of medical information raise critical issues that urgently need to be addressed, especially those related to security. Further, medical identity theft is a growing and dangerous crime. Stolen personal information can have negative financial impacts, and cuts to the very core of personal privacy. The medical identity theft is a growing crime, which already costs billions of dollars each year, and altered medical information can put a person’s health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of handheld devices to store, access, and transmit medical information is outpacing the privacy and security protections on those devices. Therefore, the authenticity of the information and related medical images is vital as they form the basis of inference for diagnostic purposes. In such applications, tamper proofing and guaranteed originality of medical data/information is achieved by embedding various watermark(s) which must be secure and robust against malicious attacks. Researchers are using watermarking and cryptography to disseminate the security of the medical data. Further, researchers are using watermarking techniques in the field of healthcare for addressing the health data management issues. It includes source and data authentication, efficient image archiving and retrieval, save bandwidth, and highlighting of diagnostically significant regions.

The objective of this Special Section in IEEE Access is to attract high-quality research articles and reviews that promote research and reflect the most recent advances in addressing and focusing the information security and privacy issues in telemedicine as well as other emerging areas. Authors are invited to submit original research and high-quality survey articles on topics including, but not limited to:

  • Watermarking, steganography, hidden data
  • Cryptographic algorithms/protocols
  • Electronic and Information security
  • Imaging
  • Health data management
  • Medical imaging modalities
  • Security and privacy of medical data
  • Systems and network security
  • Protection systems/ mechanism against patient identity theft
  • Bio‐signal Processing
  • display or secure transmission of images
  • Medical images processing
  • Biometrics
  • Image data compression
  • Patient data delivery over unsecured channels
  • Cyber Security in Telemedicine
  • Medical information security
  • Chaotic systems for privacy issues of medical data in healthcare centers

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Amit Kumar Singh, Jaypee University of Information Technology (JUIT), India

Guest Editors:

  1. S.K. Singh, Indian Institute of Technology (Banaras Hindu University), India
  2. Zhihan Lv, Chinese Academy of Science, China
  3. Charlie (Seungmin) Rho, Sungkyul University, South Korea
  4. Xiaojun Chang, Carnegie Mellon University, USA,
  5. William Puech, University of Montpellier, France

 

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: amit_245singh@yahoo.com