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Bioengineering
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Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep...
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Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning—2nd Edition

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2878

Special Issue Editors

Prof. Dr. Hongying Meng

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Brunel University of London, London UB8 3PH, UK
Interests: biomedical signal processing; machine learning; image processing; human–computer interaction; embedded systems and communications
Special Issues, Collections and Topics in MDPI journals
Dr. Xinrong Chen

E-Mail Website
Guest Editor
1. College of Bimedical Engineering, Fudan University, Shanghai 200433, China
2. Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention, Shanghai 200030, China
Interests: medical image analysis; surgical robot; medical metaverse; medical large language models

Special Issue Information

Dear Colleagues,

With the continuous advancement of technology, biomedical imaging, including radiographic images (CT, MRI, PET, SPECT, etc.), pathological images, ophthalmic images (Optical Coherence Tomography—OCT, OCT Angiography—OCTA, fundus photography, and fluorescein angiography), neuroimaging (MEG, EEG, etc), microscopy imaging, protein images, and other related biomedical images, are playing an increasingly important role in assisting clinical disease diagnosis, treatment decisions, and scientific research. With the continuous enrichment of large-scale image datasets and the ongoing advancement of cutting-edge parallel graphics processing units, advanced image processing techniques, particularly the integration of biomedical imaging and AI, hold the potential to further enhance diagnostic efficiency and accuracy. This progress is expected to significantly advance scientific research in the field of biomedical imaging.

Advanced image processing techniques, particularly AI-based methods integrating deep learning, have been widely applied to various tasks in biomedical images. These tasks range from image classification, image segmentation, image reconstruction, image super-resolution, image registration, and image fusion to disease classification, lesion detection, and survival prediction. However, the challenges surrounding the use of AI in biomedical image analysis still require further resolution.

We are pleased to invite contributions of this Special Issue, including experimental and theoretical results of new approaches and applications in biomedical imaging. This Special Issue will focus on articles and cutting-edge technology reviews that apply cutting-edge techniques to biomedical image processing and applications. Topics of interest include the construction of data-efficient deep learning models to address the demands of large datasets, the establishment of models with efficient data annotation, the enhancement of algorithm robustness and interpretability to create high-confidence models, and the development of more efficient and advanced algorithms for specific tasks.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but not limited to, the following:

  1. Advanced image processing techniques applied to biomedical imaging: Image segmentation, image reconstruction, image super-resolution, image registration, and image fusion.
  2. Advanced application technologies based on biomedical imaging: Image and disease classification, object and lesion detection, organ region and marker localization, organ and structure segmentation, survival prediction, radiation therapy planning, assistive treatment, surgical navigation, innovative approaches in large model techniques, and the fusion of biomedical imaging and multimodal information.
  3. Data-efficient models based on biomedical imaging: Training methods based on limited annotated data (unsupervised learning, semi-supervised learning, self-supervised learning, and weakly supervised learning), efficient domain adaptation models and approaches, and efficient data annotation models and approaches.
  4. Applications of novel imaging and imaging techniques in biomedical and engineering fields: Cutting-edge imaging techniques such as super-resolution imaging, fast image reconstruction and imaging techniques, and emerging radiographic imaging; the latest imaging techniques for pathological or microscopic images; the integration of virtual reality and augmented reality technologies with AI in biomedical imaging; and applications in the biomedical field utilizing novel imaging combined with AI, including the use of protein imaging and digital signal images.
  5. Medical large language models: In recent years, the emergence of medical large language models (such as multimodal foundation models and large vision–language models) has significantly expanded the capabilities of biomedical image analysis. They support a wide range of applications, including clinical decision-making, prognostic evaluation, and personalized treatment planning. Furthermore, medical large language models are being combined with emerging imaging technologies to advance research and translational applications in areas such as protein structure analysis, dynamic biological process interpretation, and cross-modal reconstruction of digital signal imaging.

We would also consider publishing other relevant technical articles and state-of-the-art technology reviews in the field.

We look forward to receiving your contributions.

Prof. Dr. Hongying Meng
Dr. Xinrong Chen
Guest Editors

Mr. Shoffan Saifullah
Guest Editor Assistant
Email:
Faculty of Computer Science, AGH University of Krakow, 30-059 Krakow, Poland
Homepage: https://badap.agh.edu.pl/autor/saifullah-shoffan-054980
Interests: biomedical image processing; Artificial Intelligence; deep learning; medical imaging; computer vision; data science in healthcare

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-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

  • biomedical image
  • artificial intelligence
  • deep learning
  • image processing
  • medical imaging
  • computer-assisted diagnosis
  • pattern recognition
  • computer vision
  • bioengineering
  • medical large language models

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 3650 KB  
Article
EndoClean: A Hybrid Deep Learning Framework for Automated Full-Video Boston Bowel Preparation Scale Assessment
by Yan Zhu, Si-Yuan Li, Pei-Yao Fu, Zhen Zhang, Shuo Wang, Quan-Lin Li and Ping-Hong Zhou
Bioengineering 2026, 13(3), 294; https://doi.org/10.3390/bioengineering13030294 - 2 Mar 2026
Viewed by 752
Abstract
Background and Aims: Adequate bowel preparation is the cornerstone of high-quality colonoscopy. The Boston Bowel Preparation Scale (BBPS) is the gold standard for assessment, yet its application suffers from inter-observer variability and lacks a fully automated solution for entire video analysis. This study [...] Read more.
Background and Aims: Adequate bowel preparation is the cornerstone of high-quality colonoscopy. The Boston Bowel Preparation Scale (BBPS) is the gold standard for assessment, yet its application suffers from inter-observer variability and lacks a fully automated solution for entire video analysis. This study proposes EndoClean, a novel, fully automated deep learning framework designed to compute the full-segment BBPS score from colonoscopy videos, aiming to provide a standardized, objective, and near expert-level assessment. Methods: EndoClean integrates three distinct models: frame selection, anatomical segmentation, and BBPS scoring. Its performance was rigorously evaluated against a reference standard established by senior experts and compared with junior endoscopists. We assessed assessment precision, inter-rater agreement (quadratic weighted Kappa), and consistency across all colonic segments. Results: The EndoClean system demonstrated superior reliability, achieving a global accuracy of 97.8% for the total BBPS score, with satisfying agreement with senior experts (κ = 0.984; 95% CI: 0.976–0.989). Notably, EndoClean performed significantly better than junior endoscopists in overall BBPS agreements (κ: 0.984 vs. 0.949, p < 0.001) and overall accuracy (97.8% vs. 94.6%, p = 0.037). In segment-specific analysis, the EndoClean surpassed junior doctors particularly in the transverse colon (Accuracy: 97.5% vs. 90.4%, p < 0.001) and effectively reduced misclassifications in clinically ambiguous intermediate scores. For binary adequacy classification, the system achieved a sensitivity of 98.2% and a specificity of 97.3%. Conclusions: EndoClean represents a robust solution in automated quality control, demonstrating performance comparable to senior experts in bowel preparation assessment. By significantly reducing the variability seen in junior endoscopists and providing objective, full-video BBPS scoring, this framework offers a viable, standardized, and efficient solution for clinical practice and large-scale quality monitoring. Full article
(This article belongs to the Special Issue Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning—2nd Edition)
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14 pages, 562 KB  
Article
Improving Normal/Abnormal and Benign/Malignant Classifications in Mammography with ROI-Stratified Deep Learning
by Kenji Yoshitsugu, Kazumasa Kishimoto and Tadamasa Takemura
Bioengineering 2026, 13(2), 206; https://doi.org/10.3390/bioengineering13020206 - 12 Feb 2026
Viewed by 817
Abstract
Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a [...] Read more.
Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal–abnormal and benign–malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several DL models, including ResNet, EfficientNet, SwinTransformer, ConvNeXt, and MobileNet. For experimentation, we used the publicly available VinDr., CDD-CESM, and DMID datasets. Our comparison with prediction results obtained without ROI-based stratification demonstrated that the utility of considering ROI presence to enhance diagnostic accuracy in mammography increases along with the data volume. These findings support the usefulness of our stratification approach, particularly as a dataset’s size grows. Full article
(This article belongs to the Special Issue Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning—2nd Edition)
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14 pages, 2350 KB  
Article
Epileptic Seizure Detection Using Hyperdimensional Computing and Binary Naive Bayes Classifier
by Xindi Huang, Hongying Meng and Zhangyong Li
Bioengineering 2025, 12(12), 1327; https://doi.org/10.3390/bioengineering12121327 - 5 Dec 2025
Cited by 1 | Viewed by 798
Abstract
Epileptic seizure (ES) detection is critical for improving clinical outcomes in epilepsy management. While intracranial EEG (iEEG) provides high-quality neural recordings, existing detection methods often rely on large amounts of data, involve high computational complexity, or fail to generalize in low-data settings. In [...] Read more.
Epileptic seizure (ES) detection is critical for improving clinical outcomes in epilepsy management. While intracranial EEG (iEEG) provides high-quality neural recordings, existing detection methods often rely on large amounts of data, involve high computational complexity, or fail to generalize in low-data settings. In this paper, we propose a lightweight, data-efficient, and high-performance approach for ES detection based on hyperdimensional computing (HDC). Our method first extracts local binary patterns (LBPs) from each iEEG channel to capture temporal–spatial dynamics. These binary sequences are then mapped into a high-dimensional space via HDC for robust representation, followed by a binary Naive Bayes classifier to distinguish ictal and inter-ictal states. The proposed design enables fast inference, low memory requirements, and suitability for hardware implementation. We evaluate the method on the SWEC-ETHZ iEEG short-term dataset. In one-shot learning, it achieves 100% sensitivity and specificity for most patients. In few-shot learning, it maintains 98.88% sensitivity and 93.09% specificity on average. The average latency is 4.31 s, demonstrating that it is much better than state-of-the-art methods. These results demonstrate the method’s potential for efficient, low-resource, and high-performance ES detection. Full article
(This article belongs to the Special Issue Diagnostic Biomedical Image and Processing with Artificial Intelligence and Deep Learning—2nd Edition)
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