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Medical Image Analysis for Computer-Aided Diagnosis and Therapy

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 491

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Editors

Dr. Junyeong Kim

E-Mail Website
Guest Editor

Department of Artificial Intelligence, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: multi-modal AI; visual-language reasoning; medical AI; computer vision
Special Issues, Collections and Topics in MDPI journals

Dr. Sunjae Yoon

E-Mail Website
Guest Editor

School of Electrical Engineering, Korea Advanced Institute and Science and Technology, Daejeon 34141, Republic of Korea
Interests: generative AI; diffusion model; image/video generation; multimodality; 3D reconstruction

Special Issue Information

Dear Colleagues,

Rapid advancements in artificial intelligence and deep learning have revolutionized the field of medical imaging, enabling more accurate, efficient, and automated analysis aiding computer-aided diagnosis and therapy planning. Medical image analysis plays a critical role in detecting, characterizing, and monitoring various diseases, supporting clinicians in making timely and precise decisions; however, challenges remain in terms of developing robust, interpretable, and generalizable AI-driven methods that can seamlessly integrate into clinical workflows

This Special Issue aims to exlore the latest innovations in medical image analyasis for computer-aided diagnosis and therapy, briging together cutting-edge research on AI-driven methodologies, multi-modal image fusion, real-time processing, and explainable AI in medical applications. We welcome original research and review articles on areas of interest including, but not limited to, the following topics:

AI and deep learning for medical image segmentation, detection, and classification;
Multi-modal and multi-scale medical image analysis;
Image-guided interventions and therapy planning;
Real-time and edge AI for medical imaging applications;
Explanable and trustworthy AI in medical imaging;
Data-efficient learning—weakly supervised, semi-supervised, and self-supervised approaches;
AI-driven radiomics and radiogenomics for disease characterization;
Challenges in clinical translation, validation, and deployment of AI models.

We invite researchers and practitioners to contribute novel insights and practical applications that push the boundaries of medical image analysis for improved diagnosis and treatment outcomes.

Dr. Junyeong Kim
Dr. Sunjae Yoon
Guest Editors

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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 analysis
compuer-aided diagnosis
image-guided therapy
multi-modal imaging
explainable AI
radiomics and radiogenomics

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Published Papers (2 papers)

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Research

22 pages, 5059 KB

Open AccessArticle

Preoperative Spatial Risk Mapping of Glioblastoma Recurrence: A Radiomics-Based Framework for Surgical Planning

by Silvia Seoni, Federica La Paglia, Matteo Salvi, Alberto Morello, Greta Bergoglio, Diego Garbossa, Massimo Salvi and Fabio Cofano

Appl. Sci. 2026, 16(14), 7344; https://doi.org/10.3390/app16147344 - 22 Jul 2026

Abstract

Glioblastoma recurrence remains nearly inevitable despite maximal resection and adjuvant therapy, with most relapses occurring within or adjacent to the original tumor site. Conventional MRI underestimates tumor infiltration beyond contrast-enhancing margins, limiting preoperative identification of peritumoral regions at higher risk of recurrence. We developed a radiomics-based machine-learning framework to generate preoperative spatial recurrence risk maps from routine MRI. Preoperative T1-weighted contrast-enhanced and FLAIR images from 79 patients with glioblastoma were analyzed. The cohort was divided at the patient level into a training set (n = 63) and an independent test set (n = 16). Using a balanced spatial ROI sampling strategy, local radiomic features were extracted from tumor and peritumoral regions and linked to recurrence sites identified on follow-up MRI acquired after at least 12 months after surgery. A CatBoost classifier achieved an AUC of 0.743 and a recall of 0.856 on an independent test set. The framework also generated preoperative probability maps that showed qualitative spatial correspondence with observed recurrence locations. These findings indicate that radiomic patterns from standard preoperative MRI may contain spatially localized information associated with future relapse. The proposed approach supports the feasibility of preoperative spatial risk stratification and may provide useful information for surgical planning and subsequent treatment strategies. Full article

(This article belongs to the Special Issue Medical Image Analysis for Computer-Aided Diagnosis and Therapy)

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23 pages, 1342 KB

Open AccessArticle

QEEF: A Quantitative Explainability Evaluation Framework for CNN and Vision Transformer-Based Segmentation Models in Dental Images

by Vincent Majanga, Ernest Mnkandla, Yifan Luo and Daniel Oladele

Appl. Sci. 2026, 16(14), 7133; https://doi.org/10.3390/app16147133 - 16 Jul 2026

Viewed by 112

Abstract

Deep learning methods have exemplified performance in various dental image segmentation tasks. However, their interpretability inadequacy remains a hindrance to clinical adoption. This study introduces a quantitative explainability-driven evaluation framework to systematically assess and compare convolutional and transformer-based segmentation models in dental imaging. Specifically, we evaluate a convolutional Watershed Encoder–Decoder Neural Network (WEDN) and the hybrid Vision Transformer U-Net (ViTUNet) model using both qualitative and quantitative explainability metrics. The qualitative analysis employs Grad-CAM, occlusion sensitivity, and attribution-based visualization techniques to assess model decision patterns. Quantitatively, we define and apply explainability metrics, including Dice coefficient, boundary Dice, sparsity, and faithfulness metric scores, to independently evaluate segmentation performance and explanation quality. The experimental results show that ViTUNet achieves superior spatial localization, with higher Dice (

0.765

) and boundary Dice (

0.421

), compared to the WEDN model (

0.664

and

0.221

), indicating improved lesion delineation in dental images. Moreover, the ViTUNet model exhibits higher sparsity scores in attribution maps, indicating more concentrated and structured interpretability regions, while the WEDN model demonstrates higher faithfulness scores under perturbation-based evaluations, thus reflecting stronger dependence on localized feature representations. These findings highlight a key distinction between convolutional and transformer-based architectures with predictive behavior and explanation characteristics. Notably, the results further indicate that gradient-based explanation methods are more stable for transformer-based models, while perturbation-based methods better capture the decision logic of convolutional networks. In general, this study establishes an integrated framework for collectively evaluating segmentation performance and explainability, allowing more transparent and clinically reliable deployment of deep learning models in dental imaging. Full article