Home / Journals American Journal of Computer Science and Technology / Practical Applications of Deep Learning Methods in Medical Image Analysis
Practical Applications of Deep Learning Methods in Medical Image Analysis
Submission DeadlineMay 10, 2020

Online submission system: http://www.sciencepublishinggroup.com/login

Lead Guest Editor
Dimitrij Shulkin
RobotDreams, Hamburg, Germany
Guest Editors
  • Samuel Abramov
    Robot Dreams, Hamburg, Germany
  • Ivan Panshin
    Promobot, Warminster, Pennsylvania, USA
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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