posted by user: zhangyudong || 1621 views || tracked by 4 users: [display]

CMES_RADLMSA 2020 : CMES_Recent Advances on Deep Learning for Medical Signal Analysis (IF: 0.796)

FacebookTwitterLinkedInGoogle

Link: http://www.techscience.com/CMES/special_detail/radlmsa
 
When N/A
Where N/A
Abstract Registration Due Apr 1, 2020
Submission Deadline May 1, 2020
Notification Due Jun 1, 2020
Final Version Due Jul 1, 2020
Categories    deep learning   artificial inteligence   transfer learning   medical signal analysis
 

Call For Papers

Special Issue: “Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA)”
• Special Issue Editors

• Special Issue Information

Deadline for manuscript submissions: March 01, 2020


Special Issue Editors

Guest Editor
Prof. Yu-Dong Zhang (Eugene), University of Leicester, UK
Prof. Zhengchao Dong, Columbia University, USA
Prof. Juan Manuel Gorriz, Cambridge University, UK/ University of Granada, Spain
Prof. Carlo Cattani, Tuscia University (VT), Italy
Prof. Ming Yang, Children’s Hospital of Nanjing Medical University, China

Special Issue Information

Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains, e.g., prediction, classification, detection, segmentation, diagnosis, interpreation, reconstruction, etc. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications.

The accelerating power of deep learning in diagnosing disease and analyzing medical data will empower physicians and speed-up decision making in clinical environments. Application of modern medical instruments and digitalization of medical care generated large amounts of biomedical information in recent years. However, new deep learning methods and computational models for efficient data processing, analysis, and modelling with the generated data is important for clinical applications and in understanding the underlying biological process.

The purpose of this special issue in the journal “CMES - Computer Modeling in Engineering and Sciences” aims to embrace the adoption, integration, and optimization of deep learning in medical signal analysis, providing the reader with an overview of this emerging technology and its unique applications and challenges in the domain of medical signal analysis.

Scopes (but are not limited to) the following:
• Theoretical understanding of deep learning in biomedical engineering;
• Transfer learning and multi-task learning;
• Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography);
• Joint semantic segmentation, object detection and scene recognition on biomedical images;
• 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.;
• Optimization by deep neural networks, Multi-dimensional deep learning;
• New model or new structure of convolutional neural network;
• Visualization and explainable deep neural network in medical signal analysis;

Related Resources

MIUA 2021   MEDICAL IMAGE UNDERSTANDING AND ANALYSIS
DLMIA 2020   Deep Learning on Medical Image Analysis - Journal of Imaging (ESCI)
ML_BDA 2021   Special Issue on Machine Learning Technologies for Big Data Analytics
ACM--ICDLT--Ei, Scopus 2021   ACM--2021 5th International Conference on Deep Learning Technologies (ICDLT 2021)--Ei Compendex, Scopus
SCOPUS-CMVIT 2021   5th International Conference on Machine Vision and Information Technology (CMVIT 2021)
DEEP-BDB 2021   The 2nd International Conference on Deep Learning, Big Data and Blockchain
Spinger MMSJ: DL MM healthcare 2020   Deep Learning for Multimedia Healthcare
ML4Music 2021   Special Issue: Machine Learning Applied to Music/Audio Signal Processing (Electronics)
SDLDIP 2021   Special Issue on Sensors and Deep Learning for Digital Image Processing
MMSys 2021   ACM Multimedia Systems Conference