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Diagnostics
Special Issues
Artificial Intelligence in Medical Image Analysis and Clinical Decision Support
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Artificial Intelligence in Medical Image Analysis and Clinical Decision Support

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 December 2026 | Viewed by 334

Special Issue Editors

Dr. Zahid Ullah

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, Republic of Korea
Interests: computer vision; medical image processing; deep learning
Dr. Tahir Mahmood

E-Mail Website
Guest Editor
Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea
Interests: computational pathology; deep learning; neural network; image classification; medical imaging; robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has rapidly transformed medical image analysis and clinical decision support systems, enabling automated analysis with improved accuracy, efficiency, and reproducibility. Advances in deep learning, vision transformers, multimodal learning, and continual learning have significantly enhanced tasks such as segmentation, detection, classification, image reconstruction, and disease prognosis across various imaging modalities, including MRI, CT, ultrasound, X-ray, and histopathology.

This Special Issue aims to compile cutting-edge research that advances AI-driven methodologies for medical image analysis and their integration into clinical workflows. We welcome contributions that address algorithmic innovation, robustness and generalization, explainability, multimodal fusion, real-world validation, and deployment challenges in healthcare environments.

We seek original research articles and comprehensive review papers. Topics of interest include, but are not limited to, the following: medical image segmentation and classification; transformer-based architectures; self-supervised and continual learning; multimodal AI systems; federated learning; uncertainty estimation; explainable AI; and AI-assisted clinical decision support systems.

I look forward to receiving your contributions.

Dr. Zahid Ullah
Dr. Tahir Mahmood
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 250 words) can be sent to the Editorial Office for assessment.

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 analysis
  • deep learning
  • vision transformers
  • medical image segmentation
  • clinical decision support
  • multimodal learning
  • explainable AI
  • continual learning
  • federated learning
  • healthcare AI

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Published Papers (1 paper)

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Research

22 pages, 7835 KB  
Article
CMT-BUSNet: Adaptive Fusion-Based Triple-Branch Hybrid Architecture for Explainable Breast Ultrasound Tumor Segmentation
by Hüseyin Kutlu and Cemil Çolak
Diagnostics 2026, 16(8), 1203; https://doi.org/10.3390/diagnostics16081203 - 17 Apr 2026
Viewed by 102
Abstract
Background/Objectives: This study proposes CMT-BUSNet, a hybrid architecture integrating CNN, Mamba, and Transformer branches for breast ultrasound tumor segmentation with built-in explainability. Methods: CMT-BUSNet employs a CNN-anchored hierarchical parallel encoder where Mamba and Transformer branches process CNN-derived features in parallel, fused through an [...] Read more.
Background/Objectives: This study proposes CMT-BUSNet, a hybrid architecture integrating CNN, Mamba, and Transformer branches for breast ultrasound tumor segmentation with built-in explainability. Methods: CMT-BUSNet employs a CNN-anchored hierarchical parallel encoder where Mamba and Transformer branches process CNN-derived features in parallel, fused through an Adaptive Feature Fusion Module (AFFM) with Dense Nested Decoder and Boundary-Aware Composite Loss. Five-fold cross-validation on BUS-BRA (N = 1875) compared nine architectures under identical protocols, plus nnU-Net v2 trained with its default self-configuring protocol as a benchmark. External evaluation used the BUSI dataset (N = 647). Results: CMT-BUSNet achieved DSC = 0.9037 ± 0.0047 on BUS-BRA with higher boundary delineation metrics than nnU-Net v2, which was trained under a different self-configuring protocol (B-IoU: 0.611 vs. 0.557; HD95: 10.07 vs. 13.54 pixels), despite nnU-Net’s marginally higher DSC (0.9108). On BUSI, CMT-BUSNet (DSC = 0.6709) yielded higher scores than nnU-Net (0.5579) across all metrics under zero-shot transfer, though the two methods were trained under different protocols. Training-based ablation confirmed each component’s contribution, and quantitative XAI validation demonstrated attribution faithfulness (nEAR = 2.82×) and uncertainty–error correlation (r = 0.39). Conclusions: CMT-BUSNet achieves competitive accuracy with higher boundary metrics, preliminary cross-dataset transferability, and built-in interpretability relative to nnU-Net (noting different training protocols). Internal validation folds are image-disjoint but not guaranteed to be patient-disjoint, which should be considered when interpreting the reported metrics. Multicenter validation is required before clinical deployment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Image Analysis and Clinical Decision Support)
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