[visionlist] JMI Special Section on Radiomics and Deep Learning - Deadline May 1st
Despina.Kontos at uphs.upenn.edu
Mon Apr 17 14:13:20 EDT 2017
A reminder for the upcoming deadline on May 1st, for the JMI Special Issue on “Radiomics and Deep Learning”.
We anticipate that this will be a highly cited issue, and very much encourage related submissions.
From: Journal of Medical Imaging [mailto:Journal-of-Medical-Imaging at reply.spie.org]<mailto:[mailto:Journal-of-Medical-Imaging at reply.spie.org]>
Sent: Friday, February 24, 2017 12:27 PM
To: Kontos, Despina
Subject: Seeking submissions for a special section on Radiomics and Deep Learning
View as web page<http://click.reply.spie.org/?qs=08b3ba540f1fa2d56e90b6a4ba65b0e646188e6b3800c0a972a837c49f6239f6d4bb77a18a3fe5d45a50cb5efd069195dcbee99b9e21d3f0>
[Journal of Medical Imaging]<http://click.reply.spie.org/?qs=08b3ba540f1fa2d5ad389587807e57e1cef2d6a644e342b92a7e8a0c60b9ab3f98d41942aba1d2d70cadad0d13df899ca93f3cd3160b917a>
Dear Despina Kontos,
The Journal of Medical Imaging is seeking submissions for a Special Section on Radiomics and Deep Learning<http://click.reply.spie.org/?qs=08b3ba540f1fa2d5d51f99dc18b5724720937815efc6130b6a610a9c147ee0865b021af25cfcc259485925fd95eec22b66d1c480104349d3>, guest edited by Despina Kontos (The University of Pennsylvania), Ronald M. Summers (National Institutes of Health), and Maryellen Giger (The University of Chicago).
The submission deadline is 1 May 2017.
Over the past decades, advances in imaging analytics from computer-aided diagnosis and quantitative imaging have provided the ability to extract clinically useful quantitative measures from medical imaging data with the goal to augment diagnostic interpretation. These imaging analytics, fueled by additional technological advances in computational resources, are now offering an unprecedented opportunity to rapidly extract and process vast amounts of information (i.e., radiomics) from medical images. Such information, especially when coupled with other biomedical data and high-dimensional machine learning tools, can not only yield methods for ultimate use in clinical decision making, but also contribute to discovery, offering new insights into genetic traits and molecular subtyping of disease, particularly in cancer, that can be used as precision medicine imaging biomarkers of disease prognosis and response to treatment.
This special section of the Journal of Medical Imaging seeks contributions in the form of research articles on the subject of radiomics and deep learning that highlight a wide spectrum of research areas including, but not limited to:
* quantitative image analysis
* high-dimensional feature extraction
* convolutional neural networks and deep learning
* computer-assisted diagnosis and prognosis
* machine learning and classification
* imaging genomics (radiogenomics).
See the full call for papers<http://click.reply.spie.org/?qs=08b3ba540f1fa2d57c83ebc15a5cd70c5cd9d18c46960c7123d3d80d50d914d4c8312029043aef63187e4792a2d908e0f9b40b5dcba3ba30>
Manuscripts should be submitted to SPIE according to the journal guidelines<http://click.reply.spie.org/?qs=08b3ba540f1fa2d5d00761a2909b04c8773eb34c4db4e358910111068f9b723a33ca41ed218437edfb3cb69b40aa06f1a49019987483b132> with a cover letter indicating that the submission is intended for this special section. SPIE publication policy permits manuscripts based partly or entirely on scientific content previously reported in SPIE proceedings to be submitted. In most cases, it is anticipated that the journal submission will represent a substantively expanded, refined, or otherwise revised manuscript to fully satisfy the journal's standards of significance, originality, and presentation quality, which will be assessed by a formal peer review process.
The Journal of Medical Imaging is published in the SPIE Digital Library, with freely searchable abstracts and tables of contents; articles are available via subscription or pay-per-view. Articles are available via subscription or pay-per-view; some open access articles are also available via an author-choice open access option.
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