posted by user: zhangyudong || 2016 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

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
ICLR 2022   The Tenth International Conference on Learning Representations
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
AISTATS 2022   25th International Conference on Artificial Intelligence and Statistics
Bio-inspired Deep Learning 2021   Bio-inspired Deep Learning Image and Signal Processing Pipelines in Medical Oncology - PeerJ
ASPAI 2021   3rd International Conference on Advances in Signal Processing and Artificial Intelligence
CAIML 2022   3rd International Conference on Artificial Intelligence and Machine Learning
XSA 2021   Explainable Deep Learning for Sentiment Analysis
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
DL-ASAP 2022   Pattern Recognition Letters - Deep Learning for Acoustic Sensor Array Processing