[visionlist] JMI Special Section on Radiomics and Deep Learning - Deadline May 1st

Mon Apr 17 13:13:20 -05 2017

Dear Colleagues,

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.

Thank you,
Despina

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
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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>

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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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