Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Machine Learning and Signal Processing for Biomedical Applications
share announcement

Machine Learning and Signal Processing for Biomedical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5687

Special Issue Editors

Dr. Qiegen Liu

E-Mail Website
Guest Editor
School of Information Engineering, Nanchang University, Nanchang 330031, China
Interests: deep learning for medical imaging; image processing
Dr. Dan Wu

E-Mail Website
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: biomedical signal processing; medical data analysis and modeling

Special Issue Information

Dear Colleagues,

Among the various applications of machine learning in biomedical engineering, this Special Issue focuses on extracting, analyzing and classifying various biomedical signals or images to solve a wide variety of medical diagnostic problems. Machine learning methods are widely used for case recognition in medical imaging. Due to the complexity and large capacity of biomedical data, it is necessary to further develop advanced analysis techniques and systems. Biomedical signal processing includes the analysis, enhancement and presentation of images captured via MRI, X-ray and visual imaging technologies.

Nevertheless, most state-of-the-art methods are unable to convey the actual scenario of the body part or organ. Machine learning techniques play an important role in handling biomedical signals or images, such as noise reduction and artifact removal.

The goal of this Special Issue is to publish original manuscripts and recent research on medical imaging, machine learning and signal processing techniques that can be used for sensing or imaging in biomedical applications. Topics include, but are not limited to, the following:

  • Medical image denoising and reconstruction;
  • Machine learning and multiscale models for biomedicine;
  • Image and signal processing for medical diagnosis;
  • Biomedical signal processing;
  • Biomedical signal analysis using machine learning and modeling;
  • Machine learning in computation systems;
  • Medical image registration;
  • Big or multi-modality data processing for predicting clinical outcomes.

Dr. Qiegen Liu
Dr. Dan Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • medical diagnostics
  • deep learning
  • medical imaging
  • biomedical signal processing
  • MRI
  • X-ray
  • PET

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

13 pages, 2885 KiB  
Article
Adversarial Attack and Defense in Breast Cancer Deep Learning Systems
by Yang Li and Shaoying Liu
Bioengineering 2023, 10(8), 973; https://doi.org/10.3390/bioengineering10080973 - 17 Aug 2023
Cited by 1 | Viewed by 1243
Abstract
Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women’s health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on [...] Read more.
Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women’s health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Biomedical Applications)
►▼ Show Figures

Figure 1

27 pages, 7260 KiB  
Article
MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals
by Md Shafayet Hossain, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, Zaid Bin Mahbub, Mamun Bin Ibne Reaz and Muhammad E. H. Chowdhury
Bioengineering 2023, 10(5), 579; https://doi.org/10.3390/bioengineering10050579 - 10 May 2023
Cited by 5 | Viewed by 2248
Abstract
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from [...] Read more.
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models’ performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Biomedical Applications)
►▼ Show Figures

Figure 1

Other

Jump to: Research

19 pages, 1426 KiB  
Systematic Review
Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review
by Juan P. Garcia-Mendez, Amos Lal, Svetlana Herasevich, Aysun Tekin, Yuliya Pinevich, Kirill Lipatov, Hsin-Yi Wang, Shahraz Qamar, Ivan N. Ayala, Ivan Khapov, Danielle J. Gerberi, Daniel Diedrich, Brian W. Pickering and Vitaly Herasevich
Bioengineering 2023, 10(10), 1155; https://doi.org/10.3390/bioengineering10101155 - 02 Oct 2023
Cited by 1 | Viewed by 1502
Abstract
Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this [...] Read more.
Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for Biomedical Applications)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop