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2019

Thematic series
Deep learning in biomedical engineering
Edited by Robert Koprowski

2018

Thematic series
Advanced signal processing and modeling for neuroengineering
Edited by Fei Chen, Shi-xiong Chen and Dong-mei Hao

Thematic series
Trends in e-health and m-health: technology developments for improved healthcare
Edited by Alessia Paglialonga

Thematic series
Artificial intelligence in biomedical imaging
Edited by Robert Koprowski

Thematic series
Advances in medical robotics and automation for surgery and rehabilitation
Edited by Dhanjoo Ghista and Kelvin Wong

2015

Thematic series
Biomedical engineering and the heart:
coronary blood flow, myocardial perfusion, myocardial ischemia and infarcts detection, customized stents and bypass surgery

Edited by Dhanjoo Ghista and Kelvin Wong

Thematic series
Advances in neuroimaging: insights into neurological and psychiatric disease
Cross-journal series
Edited by Kelvin Wong

Editors-in-Chief

Ervin Sejdic, University of Pittsburgh, USA
Fong-Chin Su, National Cheng Kung University, Taiwan

Aims and scope

BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering.

BioMedical Engineering OnLine is aimed at readers and authors throughout the world with an interest in using tools of the physical and data sciences, and techniques in engineering, to understand and solve problems in the biological and medical sciences. Read more.

Meet the Editor at BMES

Tony the Misfit on Flickr © CC-CY-2.0

Are you attending the 2019 BMES annual meeting in Philadelphia?  Come and meet  co-Editor-in-Chief, Prof Ervin Sejdic at the Springer Nature booth (#810) on Thursday 17th Oct from 15.00 to 15.45 pm.

Thematic series

BioMedical-Engineering OnLine has published the following thematic series:

Deep learning in biomedical engineering

Advanced signal processing and modeling for neuroengineering

Trends in e-health and m-health: technology developments for improved healthcare

Artificial intelligence in biomedical imaging

Advances in medical robotics and automation for surgery and rehabilitation

Biomedical engineering and the heart:
coronary blood flow, myocardial perfusion, myocardial ischemia and infarcts detection, customized stents and bypass surgery

Advances in neuroimaging: insights into neurological and psychiatric disease

Call for papers: Deep learning in biomedical engineering

Deep learning image © Ahmed GadDeep Learning in medicine is one of the most rapidly and new developing fields of science. Currently, almost every device intended for medical imaging has a more or less extended image and signal analysis and processing module which can use deep learning. It provides quantitative data necessary to make a diagnosis. The obtained quantitative features must be independent of the inter-subject variability and the type of medical device and, above all, must allow for reproducible results in the presence of high noise. The proposed deep learning algorithms should also ensure the independence of the results obtained by the operator of the imaging device and, to be more exact, its position relative to the patient or the parameter settings in the device. In addition, the proposed deep learning algorithms must be tailored for the diagnosis of a specific disease entity. On the other hand, they must allow for reproducible results for high inter-subject variability. These criteria make it difficult to propose a methodology for the deep learning algorithms. This special issue is dedicated to this area of knowledge.

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Our data and software availability policy

Our journal’s policy is for all data, previously unreported software or custom code described in the manuscript to be made freely available for any scientist wishing to use them for non-commercial purposes, without breaching participant confidentiality. All authors must include an ‘Availability of Data and Materials’ section in their manuscript, detailing where the data, software or code supporting their findings can be found. Authors who do not wish to share their data and materials must state that it will not be shared, and give the reason. However, these authors are still expected to make their data and materials available to reviewers on a confidential basis. Please see BioMed Central’s Editorial policies page for more information.

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