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Advances in Image-Based Decision Support Systems for Personalized Healthcare and...
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Advances in Image-Based Decision Support Systems for Personalized Healthcare and Computational Biology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1958

Special Issue Editors

Prof. Dr. Ivan Štajduhar

E-Mail Website
Guest Editor
Faculty of Engineering, University of Rijeka, Vukovarska ulica 58, Rijeka, 51000, Croatia
Interests: biomedical imaging; image processing; machine learning; data mining
Prof. Dr. Matija Milanic

E-Mail Website
Guest Editor
Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000 Ljubljana, Slovenia
Interests: medical imaging; medical image analysis; spectroscopy

Special Issue Information

Dear Colleagues,

Advancements in imaging technologies have revolutionized the landscape of healthcare and computational biology. The integration of cutting-edge methodologies, such as machine learning and foundation modelling, with biomedical imaging data holds tremendous potential for shaping the future of healthcare, offering improved diagnostics, and advancing personalized medicine.

This Special Issue welcomes original research papers, reviews, and case studies addressing a wide range of topics related to image processing and analysis in computational biology, healthcare, and medicine. Topics of interest include, but are not limited to:

  • Image analysis for personalized medicine: Novel techniques for analysing medical images and multi-omics data to tailor treatment plans and interventions to individual patients, ensuring more effective and targeted therapies.
  • Computer-aided diagnosis using imaging: Development of state-of-the-art decision support systems that leverage machine learning algorithms for accurate and early detection of various medical conditions from medical images.
  • Image segmentation and registration: Advancements in image segmentation and registration techniques to identify and align specific structures or regions of interest in medical images, enabling better visualization and precise diagnosis.
  • Image reconstruction and super-resolution: Innovations in image reconstruction and super-resolution techniques to enhance image quality, reduce artefacts, and improve resolution in various medical imaging modalities.
  • Image retrieval and biomedical imaging databases: Creation and utilization of large-scale biomedical imaging databases, as well as efficient image retrieval methods for facilitating research and clinical decision-making.
  • Interpretability and model uncertainty: Techniques to enhance the interpretability of deep learning models and address model uncertainty to foster trust and adoption of AI-driven decision support systems in clinical practice.

This Special Issue aims to bring together the latest research in diverse areas of image-based decision support systems that have transformative potential in improving patient care, diagnosis, and personalized medicine.

Prof. Dr. Ivan Štajduhar
Dr. Matija Milanic
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. Mathematics 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

  • biomedical imaging
  • radiology and optical imaging
  • multimodal imaging
  • multi-omics data fusion
  • tomographic reconstruction
  • image analysis for personalised medicine
  • image analysis for computer aided diagnosis
  • image segmentation
  • image registration
  • image reconstruction and super-resolution
  • image retrieval
  • machine learning
  • foundation modelling
  • model uncertainty and interpretability

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Published Papers (2 papers)

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16 pages, 6927 KiB  
Article
Estimation of Missing DICOM Windowing Parameters in High-Dynamic-Range Radiographs Using Deep Learning
by Mateja Napravnik, Natali Bakotić, Franko Hržić, Damir Miletić and Ivan Štajduhar
Mathematics 2025, 13(10), 1596; https://doi.org/10.3390/math13101596 - 13 May 2025
Viewed by 159
Abstract
Digital Imaging and Communication in Medicine (DICOM) is a standard format for storing medical images, which are typically represented in higher bit depths (10–16 bits), enabling detailed representation but exceeding the display capabilities of standard displays and human visual perception. To address this, [...] Read more.
Digital Imaging and Communication in Medicine (DICOM) is a standard format for storing medical images, which are typically represented in higher bit depths (10–16 bits), enabling detailed representation but exceeding the display capabilities of standard displays and human visual perception. To address this, DICOM images are often accompanied by windowing parameters, analogous to tone mapping in High-Dynamic-Range image processing, which compress the intensity range to enhance diagnostically relevant regions. This study evaluates traditional histogram-based methods and explores the potential of deep learning for predicting window parameters in radiographs where such information is missing. A range of architectures, including MobileNetV3Small, VGG16, ResNet50, and ViT-B/16, were trained on high-bit-depth computed radiography images using various combinations of loss functions, including structural similarity (SSIM), perceptual loss (LPIPS), and an edge preservation loss. Models were evaluated based on multiple criteria, including pixel entropy preservation, Hellinger distance of pixel value distributions, and peak-signal-to-noise ratio after 8-bit conversion. The tested approaches were further validated on the publicly available GRAZPEDWRI-DX dataset. Although histogram-based methods showed satisfactory performance, especially scaling through identifying the peaks in the pixel value histogram, deep learning-based methods were better at selectively preserving clinically relevant image areas while removing background noise. Full article
(This article belongs to the Special Issue Advances in Image-Based Decision Support Systems for Personalized Healthcare and Computational Biology)
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13 pages, 1702 KiB  
Article
A Gastrointestinal Image Classification Method Based on Improved Adam Algorithm
by Haijing Sun, Jiaqi Cui, Yichuan Shao, Jiapeng Yang, Lei Xing, Qian Zhao and Le Zhang
Mathematics 2024, 12(16), 2452; https://doi.org/10.3390/math12162452 - 7 Aug 2024
Cited by 1 | Viewed by 1272
Abstract
In this study, a gastrointestinal image classification method based on the improved Adam algorithm is proposed. Gastrointestinal image classification is of great significance in the field of medical image analysis, but it presents numerous challenges, including slow convergence, susceptibility to local minima, and [...] Read more.
In this study, a gastrointestinal image classification method based on the improved Adam algorithm is proposed. Gastrointestinal image classification is of great significance in the field of medical image analysis, but it presents numerous challenges, including slow convergence, susceptibility to local minima, and the complexity and imbalance of medical image data. Although the Adam algorithm is widely used in stochastic gradient descent, it tends to suffer from overfitting and gradient explosion issues when dealing with complex data. To address these problems, this paper proposes an improved Adam algorithm, AdamW_AGC, which combines the weight decay and Adaptive Gradient Clipping (AGC) strategies. Weight decay is a common regularization technique used to prevent machine learning models from overfitting. Adaptive gradient clipping avoids the gradient explosion problem by restricting the gradient to a suitable range and helps accelerate the convergence of the optimization process. In order to verify the effectiveness of the proposed algorithm, we conducted experiments on the HyperKvasir dataset and validation experiments on the MNIST and CIFAR10 standard datasets. Experimental results on the HyperKvasir dataset demonstrate that the improved algorithm achieved a classification accuracy of 75.8%, compared to 74.2% for the traditional Adam algorithm, representing an improvement of 1.6%. Furthermore, validation experiments on the MNIST and CIFAR10 datasets resulted in classification accuracies of 98.69% and 71.7%, respectively. These results indicate that the AdamW_AGC algorithm has advantages in handling complex, high-dimensional medical image classification tasks, effectively improving both classification accuracy and training stability. This study provides new ideas and expansions for future optimizer research. Full article
(This article belongs to the Special Issue Advances in Image-Based Decision Support Systems for Personalized Healthcare and Computational Biology)
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