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Bioengineering
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Advances in Computational Imaging and Artificial Intelligence for Biomedical and...
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Advances in Computational Imaging and Artificial Intelligence for Biomedical and Clinical Applications

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1367

Special Issue Editors

Dr. Nan Meng

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Guest Editor
Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
Interests: computational optics and imaging; from algorithms to applications and medical image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Jason Pui Yin Cheung

E-Mail Website
Guest Editor
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
Interests: computer-aided medicine; computational orthopedics; clinical medicine; pediatric spine deformity; scoliosis genetics; artificial intelligence and modeling; medical imaging and clinical phenotyping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junming Shao

E-Mail Website
Guest Editor
Data Mining Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: data mining; machine learning; neuroimaging; natural language processing (NLP)

Special Issue Information

Dear Colleagues,

The rapid advancements in computational imaging and artificial intelligence (AI) have revolutionized the biomedical and clinical fields, significantly enhancing diagnostic accuracy, treatment planning, and patient outcomes. By integrating cutting-edge computational techniques with innovative AI methods, researchers and clinicians are paving the way toward a new era of precision medicine and personalized healthcare.

This Special Issue, "Advances in Computational Imaging and Artificial Intelligence for Biomedical and Clinical Applications," seeks to showcase pioneering research and developments at the intersection of computational imaging technologies and artificial intelligence. We invite original research articles, comprehensive reviews, and innovative case studies that demonstrate significant advancements in methodologies, applications, and translational outcomes.

Areas of particular interest include, but are not limited to, the following:

  • Novel computational imaging techniques for enhanced biomedical diagnostics
  • Brain-inspired AI methodologies and their clinical implications
  • AI-driven image reconstruction, enhancement, and analysis in clinical settings
  • Multi-modal data fusion strategies for improved clinical decision-making
  • Applications of AI in surgical planning, robotic guidance, and intraoperative navigation
  • Deep learning approaches for the interpretation and analysis of biomedical imaging data
  • Ethical considerations and interpretability in AI-driven biomedical and clinical applications

This Issue aims to foster interdisciplinary collaboration among experts in bioengineering, computer science, clinical medicine, neuroscience, and related fields. By highlighting groundbreaking research and fostering dialogue among diverse scientific communities, we seek to accelerate the translation of computational imaging and AI technologies into real-world clinical solutions, ultimately benefiting patient care and healthcare outcomes globally.

Dr. Nan Meng
Dr. Jason Pui Yin Cheung
Prof. Dr. Junming Shao
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. 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

  • computational imaging
  • artificial intelligence
  • deep learning
  • medical image analysis
  • medical diagnosis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

21 pages, 2336 KB  
Article
Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization
by Roberta Fusco, Vincenza Granata, Teresa Petrosino, Paolo Vallone, Maria Assunta Daniela Iasevoli, Mauro Mattace Raso, Sergio Venanzio Setola, Davide Pupo, Gerardo Ferrara, Annarita Fanizzi, Raffaella Massafra, Miria Lafranceschina, Daniele La Forgia, Laura Greco, Francesca Romana Ferranti, Valeria De Soccio, Antonello Vidiri, Francesca Botta, Valeria Dominelli, Enrico Cassano, Charlotte Marguerite Lucille Trombadori, Paolo Belli, Giovanna Trecate, Chiara Tenconi, Maria Carmen De Santis, Luca Boldrini and Antonella Petrilloadd Show full author list remove Hide full author list
Bioengineering 2025, 12(9), 952; https://doi.org/10.3390/bioengineering12090952 - 2 Sep 2025
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Abstract
Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the [...] Read more.
Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice. Full article
(This article belongs to the Special Issue Advances in Computational Imaging and Artificial Intelligence for Biomedical and Clinical Applications)
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