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Diagnostics
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Artificial Intelligence in Biomedical Imaging and Signal Processing
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Artificial Intelligence in Biomedical Imaging and Signal Processing

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2282

Special Issue Editors

Dr. Olusola Oluwakemi Abayomi-Alli

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Guest Editor
Department of Information System, Vilnius Gediminas Technical University, Vilnius, Lithuania
Interests: artificial intelligence; computer vision; image processing; pattern recognition
Dr. Olamilekan Shobayo

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Guest Editor
School of Computing and Digital Technologies, Sheffield Hallam University, 152 Arundel Street, Sheffield S1 2NU, UK
Interests: deep learning; image processing
Dr. Modupe Odusami

E-Mail Website
Guest Editor
Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
Interests: artificial intelligence; medical image

Special Issue Information

Dear Colleagues,

Advances in signal processing techniques have transformed the use of artificial intelligence in diagnosing several diseases using biomedical images. The emergence of powerful High-Performance Computing systems (HPCs) has opened new doors for the processing of large amounts of medical data including images. The availability of these large image datasets has resulted in increasing applications of several deep learning techniques such as Convolutional Neural Networks and Vision Transformers for pattern recognition, disease classification, and image segmentation.  The application of these AI methods has continued to gain popularity in several areas in the medical field where imaging is used as a form of diagnosis. Several forms of images from different electronics machines has been used for this purpose including X-ray, CT, MRI, Ultrasound, Infra-red, etc. Additionally, multimodal imaging, which combines data from multiple imaging forms, is increasingly being explored to provide more comprehensive diagnostic insights and improve the accuracy of AI-driven diagnoses.

The impacts of artificial intelligence such as machine learning and deep learning algorithms have enhanced overall performance analysis by using varieties of data such as biomedical data, sound and signal data, wearable sensor data, medical records, etc. The adoption of these algorithms has improved the performance rate and computational complexity through the use of HPC, thus enhancing real-time disease diagnosis.

With all of these advances and their advantages comes several challenges such as overcoming issues of limited or imbalance class, image quality and feature variability issues; thus, there is a need for improved feature extraction and segmentation approaches. Finally, the need for advanced DL or ML techniques using efficient finetuning and parameter selection will not only achieve reliable diagnostic results but also improve model generalization and mitigate overfitting. Moreover, in the case of multimodal imaging, integrating data from different modalities presents its own set of challenges, as each modality has distinct characteristics and resolutions, making it difficult to harmonize the data for accurate feature extraction and analysis.

Despite these challenges, opportunities abound for progress in developing bespoke artificial intelligence technologies for medical images to diagnose patient outcomes. Research should focus on applying artificial intelligence methods by proposing relevant image processing techniques, enhancing feature extraction methods, and advancing segmentation and augmentation approaches.

Topics of interest include the following:

  • Artificial intelligence in medical images;
  • Real-time detection and image processing;
  • Image segmentation in medical scans;
  • AI-assisted detection tools;
  • Advanced augmentation for medical images;
  • AI Ethics: AI deployment in medical imaging;
  • Explainable AI in medical diagnosis;
  • Multimodal fusion AI for medical diagnosis.

Dr. Olusola Oluwakemi Abayomi-Alli
Dr. Olamilekan Shobayo
Dr. Modupe Odusami
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • medical image processing
  • healthcare
  • artificial intelligence
  • machine learning
  • deep learning
  • computer vision
  • AI-assisted
  • explainable ai
  • segmentation
  • augmentation
  • multimodal fusion

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Published Papers (3 papers)

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Research

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19 pages, 8503 KiB  
Article
The Teacher–Assistant–Student Collaborative and Competitive Network for Brain Tumor Segmentation with Missing Modalities
by Junjie Wang, Huanlan Kang and Tao Liu
Diagnostics 2025, 15(12), 1552; https://doi.org/10.3390/diagnostics15121552 - 18 Jun 2025
Viewed by 179
Abstract
Background: Magnetic Resonance Imaging (MRI) provides rich tumor information through different imaging modalities (T1, T1ce, T2, and FLAIR). Each modality offers distinct contrast and tissue characteristics, which help in the more comprehensive identification and analysis of tumor lesions. However, in clinical practice, [...] Read more.
Background: Magnetic Resonance Imaging (MRI) provides rich tumor information through different imaging modalities (T1, T1ce, T2, and FLAIR). Each modality offers distinct contrast and tissue characteristics, which help in the more comprehensive identification and analysis of tumor lesions. However, in clinical practice, only a single modality of medical imaging is available due to various factors such as imaging equipment. The performance of existing methods is significantly hindered when handling incomplete modality data. Methods: A Teacher–Assistant–Student Collaborative and Competitive Net (TASCCNet) is proposed, which is based on traditional knowledge distillation techniques. First, a Multihead Mixture of Experts (MHMoE) module is developed with multiple experts and multiple gated networks to enhance information from fused modalities. Second, a competitive function is formulated to promote collaboration and competition between the student network and the teacher network. Additionally, we introduce an assistant module inspired by human visual mechanisms to provide supplementary structural knowledge, which enriches the information available to the student and facilitates a dynamic teacher–assistant collaboration. Results: The proposed model (TASCCNet) is evaluated on the BraTS 2018 and BraTS 2021 datasets and demonstrates robust performance even when only a single modality is available. Conclusions: TASCCNet successfully addresses the challenge of incomplete modality data in brain tumor segmentation by leveraging collaborative knowledge distillation and competitive learning mechanisms. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Signal Processing)
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15 pages, 1669 KiB  
Article
Predicting Cognitive Decline in Motoric Cognitive Risk Syndrome Using Machine Learning Approaches
by Jin-Siang Shaw, Ming-Xuan Xu, Fang-Yu Cheng and Pei-Hao Chen
Diagnostics 2025, 15(11), 1338; https://doi.org/10.3390/diagnostics15111338 - 26 May 2025
Viewed by 364
Abstract
Background: Motoric Cognitive Risk Syndrome (MCR), defined by the co-occurrence of subjective cognitive complaints and slow gait, is recognized as a preclinical risk state for cognitive decline. However, not all individuals with MCR experience cognitive deterioration, making early and individualized prediction critical. [...] Read more.
Background: Motoric Cognitive Risk Syndrome (MCR), defined by the co-occurrence of subjective cognitive complaints and slow gait, is recognized as a preclinical risk state for cognitive decline. However, not all individuals with MCR experience cognitive deterioration, making early and individualized prediction critical. Methods: This study included 80 participants aged 60 and older with MCR who underwent baseline assessments including plasma biomarkers (β-amyloid, tau), dual-task gait measurements, and neuropsychological tests. Participants were followed for one year to monitor cognitive changes. Support Vector Machine (SVM) classifiers with different kernel functions were trained to predict cognitive decline. Feature importance was evaluated using the weight coefficients of a linear SVM. Results: Key predictors of cognitive decline included plasma β-amyloid and tau concentrations, gait features from dual-task conditions, and memory performance scores (e.g., California Verbal Learning Test). The best-performing model used a linear kernel with 30 selected features, achieving 88.2% accuracy and an AUC of 83.7% on the test set. Cross-validation yielded an average accuracy of 95.3% and an AUC of 99.6%. Conclusions: This study demonstrates the feasibility of combining biomarker, motor, and cognitive assessments in a machine learning framework to predict short-term cognitive decline in individuals with MCR. The findings support the potential clinical utility of such models but also underscore the need for external validation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Signal Processing)
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Review

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33 pages, 2777 KiB  
Review
Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation
by Olamilekan Shobayo and Reza Saatchi
Diagnostics 2025, 15(9), 1072; https://doi.org/10.3390/diagnostics15091072 - 23 Apr 2025
Viewed by 1214
Abstract
Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks [...] Read more.
Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. Additionally, U-Net models for segmentation, vision transformers (ViTs) for global feature extraction, and hybrid models integrating multiple architectures are explored. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process were used, and searches on PubMed, Google Scholar, and Scopus databases were conducted. The findings highlight key challenges such as data availability, interpretability, overfitting, and computational requirements. While deep learning has demonstrated significant potential in enhancing diagnostic accuracy across multiple medical imaging modalities—including MRI, CT, US, and X-ray—factors such as model trust, data privacy, and ethical considerations remain ongoing concerns. The study underscores the importance of integrating multimodal data, improving computational efficiency, and advancing explainability to facilitate broader clinical adoption. Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Signal Processing)
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