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Practical Applications of Deep Learning Methods in Medical Image Analysis
Submission Deadline: May 10, 2020

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Dimitrij Shulkin
RobotDreams, Hamburg, Germany
Guest Editors
  • Samuel Abramov
    Robot Dreams, Hamburg, Germany
  • Ivan Panshin
    Promobot, Warminster, Pennsylvania, USA
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=303). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

Special Issue Flyer (PDF)

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Special Issue

Introduction
Modern techniques in Deep Learning help to find, identify, classify, and quantify patterns in medical images outperforming medical experts. Deep Learning is rapidly becoming a state of the art, leading to increased productivity in a variety of medical applications. There are many interesting challenges like Kaggle challenges or Grand Challenges in Biomedical Image Analysis that accelerate this development. It is time to take stock of the interim results in terms of practical applications of Deep Learning in the medical field.
Aims and Scope:
  1. Tissue Segmentation
  2. Cancer Detection
  3. Digital Pathology
  4. Image Recognition
  5. Computational Diagnostics
  6. Classification
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