Advanced Deep Learning Methods for Biomedical Image Analysis

  in Special Issue   Posted on June 21, 2019

Information for the Special Issue

Submission Deadline: Thu 01 Aug 2019
Journal Impact Factor : 5.287
Journal Name : Neural Networks
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/neural-networks/call-for-papers/special-issue-on-advanced-deep-learning-methods
Journal & Submission Website: https://www.journals.elsevier.com/neural-networks

Special Issue Call for Papers:

A Special Issue for Neural Networks

Biomedical processing involves the analysis of heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction and brain activity to provide useful information upon which clinicians can make decisions. It furthers emphasis on practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.

Large amount of biomedical information and health data (particularly images) was gathered these years. However, how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the collected data is important for clinical applications and in understanding the underlying biological process.

Deep neural network is a subset of machine learning, using a model inspired by the structure of the brain. It has been rapidly developed recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing neural-network layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.

The importance of our special issue is to bring the latest theoretical and technical advancements of deep learning to biomedical image and health data analysis. Meanwhile, the investigations on the applications of deep learning to biomedical image and health data analysis may bring the reflect of improving the models of deep neural network.

The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images and other health data, by publishing high-quality research articles and reviews/tutorials in this rapidly growing interdisciplinary field.

Main Topics include:

  • Theoretical understanding of deep learning in biomedical engineering
  • Transfer learning, disentangling task transfer learning, and multi-task learning
  • Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images
  • Adversarial training on biomedical images and other health data
  • Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
  • Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.)
  • Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
  • Optimization by deep neural networks, design of new loss functions (e.g., rank-based loss function)
  • New model or improved model of convolutional neural network, (e.g., ResNet, DenseNet, Google Inception, etc.)
  • Visualization and explainable deep neural network.

Submission Procedure:

Prospective authors should follow the standard author instructions for Neural Networks, and submit manuscripts online at http://ees.elsevier.com/neunet/. The authors should select \” ADLMBIA\” when they reach the \”Article Type\” step and the \”Request Editor\” step in the submission process.

Important Dates:

  • March 1, 2019 — Start of submission period
  • August 1, 2019 — Deadline for submission
  • October 1, 2019 — First review notification
  • December 1, 2019 — Final Acceptance notification to authors
  • March, 2020 — Publication

Guest Editors:

Yu-Dong Zhang

Professor of Knowledge Discovery and Machine Learning, Department of Informatics, University of Leicester, UK (yudongzhang@ieee.orgyz461@le.ac.uk)

Francesco Carlo Morabito

Professor of Electrical Engineering, Chair Industrial Engineering Program, Vice-Rector for Internationalization, University \”Mediterranea\” of Reggio Calabria, Italy (morabito@unirc.it)

Dinggang Shen

Professor of Radiology and BRIC, Director of Center for Image Analysis and Informatics, Director of UNC IDEA Group, Director of BRIC Image Analysis Core (dinggang_shen@med.unc.edu)

Khan Muhammad

Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea (khanmuhammad@sju.ac.kr)

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