Special Issue "Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA)"

Deadline: 31 August 2020
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Guest Editors
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

Summary

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.



Published Papers
  • Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization
  • Abstract Currently, functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders. If one brain disease just manifests as some cognitive dysfunction, it means that the disease may affect some local connectivity in the brain functional network. That is, there are functional abnormalities in the sub-network. Therefore, it is crucial to accurately identify them in pathological diagnosis. To solve these problems, we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization (GNMF). The dynamic functional networks of normal subjects and early mild cognitive impairment (eMCI) subjects were vectorized and the functional connection… More
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