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Deep Learning in Biomedical Informatics
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Deep Learning in Biomedical Informatics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 14653

Special Issue Editor

Dr. Andrej Kastrin

E-Mail Website
Guest Editor
Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: knowledge discovery; machine learning; literature-based discovery; network analysis; network embedding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the enormous expansion of high-throughput technologies, life sciences have entered the big data era. Massive, high-dimensional, and heterogeneous data sets have become weaved in all areas of modern biomedical informatics, including imaging, electronic health records, sensors, and textual data. The key challenge is how to gain insight and extract useful knowledge from such data. A traditional data mining or machine learning workflow involves extensive feature engineering and domain expertise to construct useful features for the statistical representation of raw data. However, deep learning enables us to automatically learn effective representations of data with multiple levels of abstraction.

The recent decade has seen a surge in research on deep learning methods and applications in the broader domain of biomedical informatics. For example, PubMed, the largest bibliographic database in the field of biomedicine, retrieves more than 4400 records for the term “deep learning” for the last year. Despite this success, the field has not yet been thoroughly investigated and presents many challenges. It is therefore crucial to generate new ideas and develop new algorithms and methods to gain fresh insights in diverging directions.

The aim of this Special Issue is to collect both review articles and original papers describing novel methods and applications of deep learning in biomedical informatics. Papers presenting deep learning applications in the broader domain of biomedicine and healthcare are also welcome. The topics of interest for this Special Issue include but are not limited to:

  • Representation learning theory and methods;
  • Novel deep learning techniques;
  • Interpretable deep learning;
  • Deep learning for big data analytics and stream processing;
  • Next-generation network science including network embeddings;
  • Application of deep learning broadly in biomedicine, bioinformatics, and healthcare;
  • Evaluation methods and benchmark datasets.

Dr. Andrej Kastrin
Guest Editor

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. Information 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 1600 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

  • biomedical informatics
  • machine learning
  • artificial intelligence
  • representation learning
  • deep learning

Published Papers (4 papers)

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Research

10 pages, 1117 KiB  
Article
A Deep Learning Approach for Diabetic Foot Ulcer Classification and Recognition
by Mehnoor Ahsan, Saeeda Naz, Riaz Ahmad, Haleema Ehsan and Aisha Sikandar
Information 2023, 14(1), 36; https://doi.org/10.3390/info14010036 - 6 Jan 2023
Cited by 16 | Viewed by 5098
Abstract
Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on a deep learning framework [...] Read more.
Diabetic foot ulcer (DFU) is one of the major complications of diabetes and results in the amputation of lower limb if not treated timely and properly. Despite the traditional clinical approaches used in DFU classification, automatic methods based on a deep learning framework show promising results. In this paper, we present several end-to-end CNN-based deep learning architectures, i.e., AlexNet, VGG16/19, GoogLeNet, ResNet50.101, MobileNet, SqueezeNet, and DenseNet, for infection and ischemia categorization using the benchmark dataset DFU2020. We fine-tune the weight to overcome a lack of data and reduce the computational cost. Affine transform techniques are used for the augmentation of input data. The results indicate that the ResNet50 achieves the highest accuracy of 99.49% and 84.76% for Ischaemia and infection, respectively. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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15 pages, 2503 KiB  
Article
Multimodal EEG Emotion Recognition Based on the Attention Recurrent Graph Convolutional Network
by Jingxia Chen, Yang Liu, Wen Xue, Kailei Hu and Wentao Lin
Information 2022, 13(11), 550; https://doi.org/10.3390/info13110550 - 21 Nov 2022
Cited by 4 | Viewed by 2206
Abstract
EEG-based emotion recognition has become an important part of human–computer interaction. To solve the problem that single-modal features are not complete enough, in this paper, we propose a multimodal emotion recognition method based on the attention recurrent graph convolutional neural network, which is [...] Read more.
EEG-based emotion recognition has become an important part of human–computer interaction. To solve the problem that single-modal features are not complete enough, in this paper, we propose a multimodal emotion recognition method based on the attention recurrent graph convolutional neural network, which is represented by Mul-AT-RGCN. The method explores the relationship between multiple-modal feature channels of EEG and peripheral physiological signals, converts one-dimensional sequence features into two-dimensional map features for modeling, and then extracts spatiotemporal and frequency–space features from the obtained multimodal features. These two types of features are input into a recurrent graph convolutional network with a convolutional block attention module for deep semantic feature extraction and sentiment classification. To reduce the differences between subjects, a domain adaptation module is also introduced to the cross-subject experimental verification. This proposed method performs feature learning in three dimensions of time, space, and frequency by excavating the complementary relationship of different modal data so that the learned deep emotion-related features are more discriminative. The proposed method was tested on the DEAP, a multimodal dataset, and the average classification accuracies of valence and arousal within subjects reached 93.19% and 91.82%, respectively, which were improved by 5.1% and 4.69%, respectively, compared with the only EEG modality and were also superior to the most-current methods. The cross-subject experiment also obtained better classification accuracies, which verifies the effectiveness of the proposed method in multimodal EEG emotion recognition. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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15 pages, 2863 KiB  
Article
Digital Twin of a Magnetic Medical Microrobot with Stochastic Model Predictive Controller Boosted by Machine Learning in Cyber-Physical Healthcare Systems
by Hamid Keshmiri Neghab, Mohammad (Behdad) Jamshidi and Hamed Keshmiri Neghab
Information 2022, 13(7), 321; https://doi.org/10.3390/info13070321 - 1 Jul 2022
Cited by 29 | Viewed by 2823
Abstract
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber [...] Read more.
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of such cyber environments in the medical and healthcare system is not logical, as a combination of such systems with the Metaverse can lead to tremendous applications, particularly after this pandemic, in which the significance of such technologies has been proven. This is why the digital twin of a medical microrobot, which is controlled via a stochastic model predictive controller (MPC) empowered by a system identification based on machine learning (ML), has been rendered in this research. This robot benefits from the technology of magnetic levitation, and the identification approach helps the controller to identify the dynamic of this robot. Considering the size, control system, and specifications of such micro-magnetic mechanisms, it can play an important role in monitoring, drug-delivery, or even some sensitive internal surgeries. Thus, accuracy, robustness, and reliability have been taken into consideration for the design and simulation of this magnetic mechanism. Finally, a second-order statistic noise is added to the plant while the controller is updated by a Kalman filter to deal with this environment. The results prove that the proposed controller will work effectively. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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15 pages, 2733 KiB  
Article
Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix
by Yunfei Cheng, Ying Hu, Mengshu Hou, Tongjie Pan, Wenwen He and Yalan Ye
Information 2020, 11(9), 436; https://doi.org/10.3390/info11090436 - 10 Sep 2020
Cited by 6 | Viewed by 3057
Abstract
In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did [...] Read more.
In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics)
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