Deep learning in radiology – from image analysis to image reconstruction

  in Special Issue   Posted on June 15, 2019

Information for the Special Issue

Special Issue Call for Papers:

Call for Papers: Deep learning in radiology – from image analysis to image reconstruction

Scope and Purpose

Radiology imaging has become an integral part of disease diagnosis and treatment and is increasingly important. In recent decades, with the rapid development and popularization of medical imaging equipment, medical image data has been expanding. How to efficiently and accurately process these image big data, provide scientific methods and advanced technologies for screening, diagnosis, treatment planning, and efficacy evaluation in clinical medicine, is a major scientific problem that needs to be solved. Image analysis and image reconstruction are the two most important pillars in the field of medical imaging. Deep learning algorithms have demonstrated the potential in the field of medical imaging beyond traditional transform-based or optimization-based methods.

The purpose of this special issue is to demonstrate the new development and application of brain-inspired artificial intelligence algorithms, to solve the problem from image analysis and image interpretation to image reconstruction. The ultimate goal is to promote research and development of deep learning in radiology imaging and other medical data by publishing high-quality research papers in this interdisciplinary field that can profoundly impact the future of the medical industry. Potential topics include, but are not limited to:

  • Neural network framework for radiological image enhancement and reconstruction.
  • Deep learning algorithm for fast imaging (MR Image).
  • Deep neural network in radiological image (X-ray image, etc.) processing and analysis (classification, target detection and others).
  • Data mining with deep learning in radiological images.

· Personalized diagnosis and personalized treatment based on invasive interventions of deep learning and images.

· Transfer learning and multitasking for radiological image analysis.

· Antagonistic training on radiological images and other medical data.

· Online deep learning neural network.

Submission Guideline

The submitted article must be original, unpublished and not currently reviewed by other journals. Authors must mention in their cover letter for each SI manuscript that the particular manuscript is for the theme and name of Guest Editors of SI consideration so that the Guest Editors can be notified separately. Prospective authors should follow the standard author instructions for Computer Methods and Programs in Biomedicine, and submit manuscripts online at https://www.evise.com/profile/#/CMPB/login by selecting the Article Type \”VSI: DLIR\”

Important Dates

Manuscript submissions due: 20 October 2019

Guest Editors

Prof Kelvin KL Wong

Centre for Biomedical Engineering (CBME), School of Electrical and Electronic Engineering, The University of Adelaide, Australia.

E-mail: kelvin.wong@adelaide.edu.au

Prof Giancarlo Fortino

University of Calabria, Rende (CS), Italy.

E-mail: g.fortino@unical.it

Prof Jimmy Zhihua Liu

Department of Biostatistics, Harvard School of Public Health, USA.

E-mail: zhliu@jimmy.harvard.edu