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Please distribute
(Apologies for cross posting)
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CALL FOR PAPERS
Special Issue Call for Papers - Information Fusion for Medical Data:
early, late and deep fusion methods for multimodal data

https://jbhi.embs.org/special-issues/information-fusion-for-medical-data-early-late-and-deep-fusion-methods-for-multimodal-data/

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Aims and Scope
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Medical data exists in a broad range of formats, from structured data
and medical reports to 1D signals, 2D images and 3D volumes or even
higher dimensional data such as temporal 3D sequences. Think about the
assessment of the functioning of the heart, for instance. A physician
can make an auscultation and produce a report in text format; an
electrocardiogram can be made and printed in time series format, an
x-ray can be performed and saved as an image; a volume can be provided
through an angiography; temporal information can be given by
echocardiograms, 4D information can be extracted through flow MRI.
Another typical source of variability is the existence of data from
different time points, such as pre and post treatment, for instance.
This high and diverse amount of information needs to be organized and
mined in an appropriate way so that meaningful information can be
extracted. Several questions, however, arise when dealing with these
situations. Should different types of information be treated
differently? Should a common framework be derived? Are new analytic
approaches needed? It is our hope that these and other questions will
be addressed by this special issue.

In this call, we focus on sharing recent advances in algorithms and
applications that involve combining multiple sources of medical
information. Topics appropriate for this special issue include novel
supervised, unsupervised, semi-supervised and reinforcement
algorithms, new architectures, new formulations, and applications
related to medical information fusion.

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Topics of Interest
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Topics appropriate for this special issue include (but are not limited to):

-Analysis on fusion of big medical data
-Applications of multimodal learning in image and computer vision
medical areas
-Combining multiple models for medical data
-Combining multiple sources in medical data
-Cross modality learning
-Feature fusion for medical data
-Early and late fusion approaches
-Hierarchical models for medical information fusion
-Improved algorithms for medical information fusion
-Intelligent systems for medical information fusion
-Joint feature learning
-Medical multi-sensor fusion
-Multimodal metric learning for medical applications
-New models for multimodal medical data
-Transfer learning in multimodal medical data
-Deep learning with inputs from several data sources

Manuscripts must clearly delineate the role of information fusion for
medical data. The manuscript should include new contributions beyond
those made in earlier publications. Review works will not be
considered in this special issue. Contributions should be described in
sufficient detail to be reproducible on the basis of the material and
references presented in the paper.

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Guest Editors
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Andres Ortiz
Universidad de Málaga
aor...@ic.uma.es

Belur V. Dasarathy
Consultant ? Decision Systems & Information Fusion Technologies
fusion-consult...@ieee.org

Henning Müller
HES-SO, Sierre, Switzerland
henning.muel...@hevs.ch

Inês Domingues
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal
icdoming...@dei.uc.pt

Pedro H. Abreu
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal
p...@dei.uc.pt

Vince D. Calhoun
The Mind Research Network
vcalh...@mrn.org

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Key Dates
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Deadline for Submission: 31 Dec. 2018
First Reviews Due: 28 Feb. 2019
Revised Manuscript Due: 30 Apr. 2019
Final Decision: 31 May 2019

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