Content Gazette - October 2019 - 23

Call for Papers
IEEE Journal of Selected Topics in Signal Processing
Special Issue on Domain Enriched Learning for Medical Imaging
In recent years, learning based methods have emerged to complement traditional model and feature
based methods for a variety of medical imaging problems such as image formation, classification
and segmentation, quality enhancement etc. In the case of deep neural networks, many solutions
have achieved unprecedented performance gains and have defined a new state of the art. Despite
the progress, compelling open challenges remain. One such key challenge is that many learning
frameworks (notably deep learning) are purely data-driven approaches and their performance depends strongly on the quantity and quality of training image data available. When training is
limited or noisy, the performance drops sharply. Deep neural networks based approaches additionally face the challenge of often not being straightforward to interpret. Fortunately, exciting recent
progress has emerged in enriching learning frameworks with domain knowledge and signal structure. As a couple of representative examples: in image reconstruction problems, this may involve
using statistical/structural image priors; for image segmentation, shape and anatomical knowledge
(conveyed by an expert) may be leveraged, etc. This special issue invites original new contributions
that combine signal, image priors and other flavors of domain knowledge with machine learning
methods for solving medical imaging problems. Topics of interest include but are not limted to:
* Fundamental innovations in combining model based and learning based methods.

* Sparse representation and dictionary learning based methods for medical image processing
and understanding.
* Domain enriched and regularized deep learning via special network architectures and systematic integration of problem specific insights.
* Interpretable deep networks for medical imaging via techniques such as algorithm unrolling.

* Algorithmic methods that gracefully degrade with amount of training image data available
and enable robustness against selection bias.
* Example applications include image reconstruction and formation, medical image classification and segmentation, image understanding, boundary and shape analysis, registration,
quality enhancement etc. The scope encompasses all medical imaging modalities including
but not limited to MRI, X-Ray, CT, PET, ultrasound, photoacoustic imaging, various forms
of microscopy, multispectral imaging, new and emerging imaging techniques and modalities.
Important Dates:
Manuscript Submission Due:
First Review Completed:
Revised Manuscript Due:
Second Review Completed:
Final Manuscript Due:

Dec. 01st , 2019
Feb. 01st , 2020
Apr. 01st , 2020
Jun. 01st , 2020
Jul. 15th , 2020

Guest Editors (GEs):
Vishal Monga (Lead GE, Pennsylvania State University, USA, vmonga@engr.psu.edu)
Scott Acton (University of Virginia, USA, acton@virginia.edu)
Arrate Mu˜
noz Barrutia (Universidad Carlos III de Madrid, Spain, amunoz@hggm.es)
Jong Chul Ye (KAIST - Korea Advanced Institute of Science and Technology, jong.ye@kaist.ac.kr)
Abd-Krim Seghouane (University of Melbourne, Australia, abd-krim@seghouane@unimelb.edu.au)

www.signalprocessingsociety.org

[23]

OCTOBER 2019


http://www.signalprocessingsociety.org

Content Gazette - October 2019

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