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 1730
Special Issue Editors
Interests: biomedical imaging; image processing; machine learning; data mining
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
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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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