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

Submission Deadline: 31 August 2020 (closed)
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


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

  • A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning
  • Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN). However, sometimes, patients’ treatment can… More
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  • Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier
  • Abstract Alcoholism is an unhealthy lifestyle associated with alcohol dependence. Not only does drinking for a long time leads to poor mental health and loss of self-control, but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs. Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health. As their drinking increases, they become dependent on alcohol and it affects their daily lives. Therefore, it is important… More
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  • Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification
  • 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
  •   Views:2339       Downloads:1638       Cited by:7        Download PDF