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

Submission Deadline: 31 August 2020 (closed)
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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
  • An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks
  • Abstract As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from the information they wrote. In… More
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  • Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identi
  • Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More
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  • 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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