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:
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Associate Editor: Kelvin KL Wong, Western Sydney University, Australia
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
Paper submission: Contact Associate Editor and submit manuscript to:
For inquiries regarding this Special Section, please contact: Kelvin.Wong@westernsydney.edu.au