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Bioengineering
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Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second...
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Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition

A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 3851

Editors

Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Eleni Vrochidou

E-Mail Website
Guest Editor
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine/deep learning; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging and biomedical signal processing are fundamental pillars of modern healthcare, enabling the detection, diagnosis, and monitoring of a wide range of diseases. In recent years, the rapid advancement of artificial intelligence (AI), machine learning (ML), and deep learning (DL) has significantly transformed these fields, offering powerful tools for extracting meaningful patterns from complex, high-dimensional data. These technologies are increasingly being integrated into clinical workflows, contributing to improved diagnostic accuracy, personalized treatment planning, and enhanced patient outcomes.

Building upon the success of the first edition, this Special Issue, ‘Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing—2nd Edition’, aims to further explore recent developments, emerging methodologies, and real-world applications at the intersection of AI and biomedical data analysis. Particular emphasis will be placed on robust, interpretable, and clinically translatable AI systems that can effectively bridge the gap between research and practice.

We welcome high-quality original research articles, review papers, and short communications that address both theoretical advancements and practical implementations. Contributions may cover a wide range of imaging modalities (e.g., MRI, CT, ultrasound, X-ray, PET) and biomedical signals (e.g., ECG, EEG, EMG), as well as multimodal data integration approaches. Topics of interest include, but are not limited to, the following:

  • Medical image segmentation, classification, detection, and reconstruction;
  • Radiomics and quantitative imaging biomarkers;
  • Deep learning architectures and novel AI models for biomedical data analysis;
  • Multimodal data fusion combining imaging and biosignals;
  • Signal processing and pattern recognition in biomedical signals;
  • Explainable and interpretable AI (XAI) in healthcare applications;
  • AI-driven computer-aided diagnosis and clinical decision support systems;
  • Robustness, generalization, and domain adaptation in medical AI models;
  • Data standardization, harmonization, and reproducibility in biomedical imaging and signals;
  • Small-sample learning, transfer learning, and self-supervised learning in medical data;
  • Synthetic data generation and augmentation techniques;
  • Integration of AI into clinical workflows and real-world validation studies.

Mr. Georgios Lekkas
Guest Editor Assistant
E-mail: gelekka@cs.duth.gr; gxlekkas@gmail.com
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
Interests: artificial intelligence; computer vision; machine learning; signal processing; image processing; medical image analysis 

Prof. Dr. George A. Papakostas
Dr. Eleni Vrochidou
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence
  • biomedical imaging
  • biomedical signal processing
  • machine learning
  • deep learning
  • image segmentation
  • image classification
  • signal analysis
  • pattern recognition
  • medical diagnostics

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Related Special Issue

Published Papers (6 papers)

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Research

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31 pages, 1023 KB  
Article
Signal-Driven Model Order Selection for MUSIC-Based HRV Spectral Characterization
by Perla Lizeth Garza-Barrón, Alejandro Barrientos-García, Carlos Mauricio Lastre-Domínguez, Claudia Angélica Rivera-Romero, Juvenal Villanueva-Maldonado and Jorge Ulises Muñoz-Minjares
Bioengineering 2026, 13(9), 1033; https://doi.org/10.3390/bioengineering13091033 - 5 Sep 2026
Viewed by 248
Abstract
Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration [...] Read more.
Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration parameters. This work presents a methodology for the spectral characterization of HRV signals derived from ECG recordings from the DREAMER database, with emphasis on optimizing the model order of the MUSIC algorithm to improve dominant frequency localization within the physiological low-frequency (LF) and high-frequency (HF) bands. The proposed methodology included ECG signal preprocessing, R-peak detection, RR interval extraction, HRV interpolation, and spectral analysis using MUSIC, while evaluating different model orders through a signal-driven composite criterion based on AIC, MDL, ESTER, eigengap, and model complexity. The criteria were normalized using min–max normalization and combined using equal predefined weights. The results showed that the signal-driven selection of the parameter p produced recording-dependent model order configurations and different dominant frequency estimates across the analyzed stimuli. The resulting LF/HF agreement was evaluated independently after model order selection and showed non-uniform correspondence across stimuli and spectral estimators. Overall, these findings indicate that model order selection can substantially influence the spectral characterization obtained with MUSIC and provide a signal-driven framework for examining this dependence in HRV recordings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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30 pages, 5842 KB  
Article
DTARNU-Net: Dense Tiered Attention Residual Nested U-Net for CT Liver Tumor Segmentation
by Kumar P, Robert P, Parthasarathy Ramadass and Mohd Anul Haq
Bioengineering 2026, 13(9), 992; https://doi.org/10.3390/bioengineering13090992 - 27 Aug 2026
Viewed by 236
Abstract
Liver tumor segmentation is a significant task in clinical imaging that involves detecting liver tumors and distinguishing them from the surrounding liver tissue in CT scans. Precision segmentation performs important roles in the initial detection of liver cancer, treatment planning, and monitoring disease [...] Read more.
Liver tumor segmentation is a significant task in clinical imaging that involves detecting liver tumors and distinguishing them from the surrounding liver tissue in CT scans. Precision segmentation performs important roles in the initial detection of liver cancer, treatment planning, and monitoring disease development, which also supports doctors, facilitating surgeries and radiation therapy more efficiently. Meanwhile, clinical imaging and segmentation algorithms have been enhanced over the years. The currently prevailing state-of-the-art methods still face multiple difficulties, though, in obtaining precision and reliability in their outcomes. Tumors with irregular shapes, variable sizes, and densities similar to those of surrounding tissues often lead to segmentation inaccuracies and potential misdiagnoses. In this work, we tackle these challenges by developing an advanced process for precise liver tumor segmentation by utilizing CT images from the LiTS dataset. The proposed DTARNU-Net was developed, trained, validated, and evaluated exclusively using the Liver Tumor Segmentation (LiTS) benchmark dataset. No experiments were conducted on the 3D-IRCADbI dataset in this study. All quantitative and qualitative results presented in the manuscript correspond to the LiTS dataset. The LiTS dataset contains contrast-enhanced abdominal CT scans with expert-annotated liver and tumor masks. The proposed model was evaluated using patient-level training, validation, and testing partitions (9:2:2 ratio), and all experiments were independently repeated five times. Statistical significance was assessed using paired Student’s t-test (p < 0.05), and the results confirmed that the performance improvements over competing methods are statistically significant. We introduce a novel three-level pre-processing approach that significantly enhances image quality through histogram equalization, noise removal, smoothing, and sharpening. Our approach is embodied in the Dense Tiered Attention Residual Nested U-Net (DTARNU-Net), a sophisticated model combining the strengths of a Siamese network and a nested U-Net architecture. This model incorporates the ACON-ReLU residual convolution block (A-R), which improves recognition accuracy in regions with subtle changes, reducing missed detection. The presented method enhances trait collaboration and spatial data by utilizing the Brownian Motion-based Butterfly Optimization Algorithm (BM-BOA). This algorithm efficiently integrates low-level trait details with high-level semantic data. The Dense Tiered Attention Residual Module (DTSRM) additionally improves these traits to obtain more precise segmentation. The model achieved segmentation robustness of 96.78% for liver segmentation and 97.00% for liver tumor segmentation on the LiTS dataset. These outcomes indicate that the presented method performs better than the prevailing state-of-the-art methods and has the capability to help computer-assisted detection and treatment by furnishing more precise and reliable liver tumor segmentation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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42 pages, 7721 KB  
Article
Conditional Diffusion–Augmented Cascaded Multi-View Attention BiLSTM for Non-Invasive Blood Glucose Estimation from Photoplethysmography
by Chaofan Mo and Jianfeng He
Bioengineering 2026, 13(9), 974; https://doi.org/10.3390/bioengineering13090974 - 25 Aug 2026
Viewed by 487
Abstract
Photoplethysmography (PPG)-based blood glucose estimation is attractive for non-invasive monitoring, but its development is constrained by limited paired PPG–glucose data and the weak representation of glucose-related waveform variations. This study proposes a framework combining conditional diffusion-based data augmentation with a cascaded multi-view attention [...] Read more.
Photoplethysmography (PPG)-based blood glucose estimation is attractive for non-invasive monitoring, but its development is constrained by limited paired PPG–glucose data and the weak representation of glucose-related waveform variations. This study proposes a framework combining conditional diffusion-based data augmentation with a cascaded multi-view attention bidirectional long short-term memory (BiLSTM) network. Blood glucose level, heart rate, and body mass index were used as physiological conditions to generate synthetic PPG segments, while channel and temporal attention together with cascaded BiLSTM layers were used to extract discriminative spatiotemporal features. A moth–flame optimization algorithm was employed to tune key hyperparameters. Experiments were conducted on a public dataset containing 67 PPG recordings from 23 participants. The framework was evaluated using subject-wise five-fold cross-validation, ensuring that all recordings from the same participant remained within a single fold. In the primary seed-42 analysis, the proposed method achieved a recording-level RMSE of 0.59 mmol/L, an MAE of 0.38 mmol/L, a MARD of 5.48%, and Clarke Zone A and A + B proportions of 97.0% and 100%, respectively; repeating the complete evaluation with seeds 2026 and 3407 produced closely similar recording-level results. These results suggest that physiologically conditioned generative augmentation may improve PPG-based blood glucose estimation in small-sample settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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21 pages, 8652 KB  
Article
Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification
by Farchan Hakim Raswa, Muhammad Fadlurrohman, Bach-Tung Pham, Ika Candradewi, Afiahayati, Ming-Hsiang Su, Chung-I Huang, Kuo-Chen Li, Shih-Lun Chen, Yung-Hui Li and Jia-Ching Wang
Bioengineering 2026, 13(7), 765; https://doi.org/10.3390/bioengineering13070765 - 30 Jun 2026
Viewed by 822
Abstract
A deep learning model with an imbalanced mammography dataset can bias models toward common benign BI-RADS categories and reduce recognition of less frequent malignant or high-risk categories. To address this issue, we propose B2M (Benign Share Benefit to Malignant), a model-agnostic framework for [...] Read more.
A deep learning model with an imbalanced mammography dataset can bias models toward common benign BI-RADS categories and reduce recognition of less frequent malignant or high-risk categories. To address this issue, we propose B2M (Benign Share Benefit to Malignant), a model-agnostic framework for imbalance-aware multi-class BI-RADS classification in C-View mammography. B2M uses a two-phase training strategy that combines dual sampling with feature-space mixing. In Phase I, the model is trained with dual sampling, integrating instance-based and class-balanced sampling to increase minority-class representation while preserving majority-class diversity. In Phase II, the model is fine-tuned with feature-space mixing using samples from the two sampling streams. A soft-target regularization objective supervises the mixed features using labels from both streams, encouraging smoother decision boundaries across BI-RADS categories. We evaluated B2M on an imbalanced mammography cohort from the C-View EMBED dataset using stratified 5-fold cross-validation across multiple CNN backbones. C-View is a synthesized 2D mammographic image generated from 3D digital breast tomosynthesis data, capturing DBT-derived structural information while requiring less memory and computation than processing the full 3D image volume. Among these experiments, ResNeXt-50 with B2M achieved the highest balanced accuracy and Macro-F1 scores compared with the evaluated oversampling and mixing-based methods. This improvement requires an offline training-time overhead of approximately 2.81×, but it does not increase inference cost. Overall, the results suggest that B2M may be useful for imbalanced multi-class BI-RADS classification in C-View mammography. However, the findings are based on the EMBED cohort, and further validation, including external and prospective evaluation, is needed before clinical use. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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14 pages, 13480 KB  
Article
EEG–ShuffleFormer: A Multi-View Hybrid Network Integrating Time–Frequency and Raw Signal Representations for Few-Channel Motor Imagery EEG Classification
by Kang Fan, Qin Gu and Yaduan Ruan
Bioengineering 2026, 13(5), 578; https://doi.org/10.3390/bioengineering13050578 - 19 May 2026
Viewed by 561
Abstract
Electroencephalogram (EEG) signals hold significant research value in brain function decoding, disease diagnosis, and brain–computer interfaces (BCIs). Few-channel EEG recording devices feature superior portability, simple operation, and facilitated real-time monitoring implementation. However, few-channel motor imagery (MI) EEG signals inherently suffer from data scarcity [...] Read more.
Electroencephalogram (EEG) signals hold significant research value in brain function decoding, disease diagnosis, and brain–computer interfaces (BCIs). Few-channel EEG recording devices feature superior portability, simple operation, and facilitated real-time monitoring implementation. However, few-channel motor imagery (MI) EEG signals inherently suffer from data scarcity and limited spatial discriminative information, which pose critical challenges, including insufficient feature extraction and poor robustness in classification tasks. To address these issues, this paper presents EEG–ShuffleFormer, a hybrid network that integrates two complementary views of EEG signals: time–frequency representations obtained via continuous wavelet transform and the original raw signal representations. A lightweight ShuffleNet backbone extracts local features, followed by a Transformer encoder that models long-range temporal dependencies. Evaluated on the BCI Competition IV Dataset 2b, the proposed method achieves an average classification accuracy of 82.23%, with a substantial improvement on challenging subjects compared to the closest baseline method. Compared with existing methods, the proposed multi-view fusion strategy raises the performance floor while maintaining high accuracy on typical subjects, demonstrating its potential to enhance robustness for different subjects in few-channel scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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Review

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15 pages, 695 KB  
Review
Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions
by Jiangfan Yu, Sibusiso Mdletshe, Hamid Abbasi, Eryn Kwon, Samantha Holdsworth and Alan Wang
Bioengineering 2026, 13(6), 699; https://doi.org/10.3390/bioengineering13060699 - 18 Jun 2026
Viewed by 786
Abstract
Structural magnetic resonance imaging (sMRI) is progressively used to diagnose brain diseases; however, brain sMRI scans can be easily corrupted by artifacts, e.g., motion artifacts. To remove artifacts, deep learning (DL) algorithms have been extensively studied recently. However, their performance and the challenges [...] Read more.
Structural magnetic resonance imaging (sMRI) is progressively used to diagnose brain diseases; however, brain sMRI scans can be easily corrupted by artifacts, e.g., motion artifacts. To remove artifacts, deep learning (DL) algorithms have been extensively studied recently. However, their performance and the challenges currently faced in clinical practice (e.g., real-world robustness, hallucination and over-smoothing) have not been adequately studied in a quantitative manner. In this structured literature review, we quantitatively examined DL-based artifact correction studies (N = 30), retrieved from the major databases (i.e., Google Scholar, PubMed, Web of Science, and Scopus), which particularly focused on clinical-field-strength (defined as 1.5 Tesla (T) and above) sMRI in a non-pediatric setting. Our review suggests that current DL-based approaches exhibit promising fidelity measured by structural similarity (SSIM, 0.92 ± 0.05) index and peak signal-to-noise ratio (PSNR, 32.85 ± 4.53 dB). In addition, We identified the factors underlying hallucination or over-smoothing, which are associated with neural network (NN) architecture and the training process. This study also reveals the potential advantages, brought about by frequency-aware NN. Finally, we outline several future directions, including an emerging paradigm in DL, namely physics-informed NN (PINN). Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)
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