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Diagnostics
Special Issues
Advances in Medical Image Processing
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Advances in Medical Image Processing

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: 30 June 2026 | Viewed by 1563

Special Issue Editor

Dr. Zhiwei Wang

E-Mail Website
Guest Editor
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: image processing; machine learning; computer vision

Special Issue Information

Dear Colleagues,

Medical image processing has become an indispensable component of modern healthcare, enabling more accurate diagnosis, optimized treatment planning, and continuous disease monitoring. With the rapid development of artificial intelligence, this field has witnessed remarkable progress in recent years. These advances have not only enhanced the accuracy and efficiency of medical image analysis but also paved the way for personalized medicine and large-scale clinical applications.

This Special Issue will highlight the latest research and technological innovations in medical image processing, with an emphasis on both cutting-edge methodologies and their translational impact across diverse clinical scenarios. Topics of interest include novel algorithms for image enhancement, segmentation, registration, detection, classification, reconstruction, cross-modal fusion, clinical report generation, and the integration of deep learning into traditional imaging workflows. Studies emphasizing real-world clinical validation, robustness, interpretability, fairness, and generalization are strongly encouraged.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Adaptation of foundation models for medical imaging;
  • Generative AI for medical image synthesis and augmentation;
  • Advanced image segmentation, registration, and enhancement;
  • Multimodal and cross-domain image fusion;
  • Computer-aided diagnosis and detection;
  • Medical image reconstruction and quality assessment;
  • Explainable and trustworthy AI for clinical interpretability;
  • Applications in radiology, pathology, oncology, and other clinical domains.

We look forward to receiving your contributions.

Dr. Zhiwei Wang
Guest Editor

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 processing
  • foundation models
  • multimodal learning
  • computer-aided diagnosis
  • image segmentation and registration
  • generative AI
  • clinical decision support
  • representation learning
  • explainable and trustworthy AI

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

Published Papers (3 papers)

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Research

12 pages, 8863 KB  
Article
Deep Learning Reconstruction Specialized for Inner Ear: Improving Image Quality and Anatomical Structure Visualization as Compared with Conventional Hybrid-Type Iterative Reconstruction on High-Definition CT
by Masahiko Nomura, Hirona Kimata, Yuya Ito, Kenji Fujii, Naruomi Akino, Takahiro Ueda, Takeshi Yoshikawa, Daisuke Takenaka, Yoshiyuki Ozawa and Yoshiharu Ohno
Diagnostics 2026, 16(12), 1756; https://doi.org/10.3390/diagnostics16121756 - 6 Jun 2026
Viewed by 196
Abstract
Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in [...] Read more.
Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in patients with and without otologic diseases. Methods: Included in this study were 140 patients who had undergone HDCT, consisting of 32 otologic disease patients and 108 non-otologic disease patients, and 280 inner and middle ears and temporal bones were evaluated on a per ear analysis. Signal-to-noise ratios (SNRs) of the temporal bone surrounding the aural vestibule of the ear and in the vestibule as well as the cerebellar hemisphere, overall image and detailed evaluation of the visibility of anatomical landmarks in the middle and inner ear and temporal bone obtained with the two methods were assessed and statistically compared using the paired t-test or Wilcoxon’s signed-rank test. Then, receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance between two reconstruction methods. Results: Each SNR of DLR was significantly higher than that of hybrid-type IR (p < 0.05). Overall image quality and detailed visualization of each anatomical structure obtained with DLR were significantly better than those obtained with hybrid-type IR (p < 0.05). The area under the curve of DLR had no significant difference with hybrid-type IR (p = 0.18). Conclusions: DLR has superior potential to hybrid-type IR for better image quality and visualization of anatomical landmarks in middle and inner ears and temporal bones on HDCT, although diagnostic performance was not affected in clinical practice. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing)
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20 pages, 3614 KB  
Article
Synthetic Implant Migration Generation for Accuracy and Precision Evaluation of AI-Based CT-RSA in Total Hip Arthroplasty
by Hassan M. Nemati, Albin Christensson, Andreas Pettersson and Gunnar Flivik
Diagnostics 2026, 16(10), 1484; https://doi.org/10.3390/diagnostics16101484 - 14 May 2026
Viewed by 329
Abstract
Background/Objectives: Radiostereometric analysis (RSA) is the gold standard for measuring implant migration, with CT-RSA increasingly used as an alternative. To evaluate CT-RSA, it is important to assess data that include the surrounding soft tissues, rather than data from simplified phantoms, while also [...] Read more.
Background/Objectives: Radiostereometric analysis (RSA) is the gold standard for measuring implant migration, with CT-RSA increasingly used as an alternative. To evaluate CT-RSA, it is important to assess data that include the surrounding soft tissues, rather than data from simplified phantoms, while also avoiding unnecessary radiation from multiple scans. This study proposes a method for generating multiple follow-up CTs from a single post-operative CT (baseline CT) by simulating stem migration and uses it to assess an AI-based CT-RSA tool. Methods: The method involves extracting the stem implant voxels from the baseline CT, digitally translating them along the x-, y-, and z-axes, and storing the result as new follow-up CTs. The voxel spacing of the baseline CT is used to define the ground-truth translations, which are then compared with the AI-based CT-RSA results using descriptive statistics and Bland–Altman plots. Results: Using 10 patients’ baseline CTs, 780 follow-up CTs were generated. Bland–Altman analysis showed a mean difference of 0.00 mm, largest LoA −0.10 to 0.09 mm, and translational precision for zero-migration of 0.026 to 0.049 mm. Conclusions: The proposed method offers a practical alternative to phantom-based models, and the AI-based CT-RSA showed high accuracy and precision for stem translation. The study addresses translational migration only. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing)
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16 pages, 5741 KB  
Article
Hybrid Curriculum Learning for Data-Efficient Lung Nodule Detection with YOLOv11
by Yi Luo, Yike Guo, Hamed Hooshangnejad, Xue Feng, Quan Chen, Zongwei Zhou, Yaxi Chen, Yipeng Hu, Rui Zhang and Kai Ding
Diagnostics 2026, 16(10), 1441; https://doi.org/10.3390/diagnostics16101441 - 8 May 2026
Viewed by 398
Abstract
Background/Objectives: Accurate detection of pulmonary nodules on chest CT is critical for lung cancer screening, yet training robust detectors remains challenging due to the high cost of reliable annotations. In this work, we present a systematic study of curriculum learning for CT-based lung [...] Read more.
Background/Objectives: Accurate detection of pulmonary nodules on chest CT is critical for lung cancer screening, yet training robust detectors remains challenging due to the high cost of reliable annotations. In this work, we present a systematic study of curriculum learning for CT-based lung nodule detection on the enhanced LUNA25 benchmark and propose a hybrid curriculum learning framework for data-efficient optimization. Methods: Our approach estimates sample difficulty by fusing clinically interpretable handcrafted factors such as nodule size and count with model-driven signals such as prediction confidence from a teacher model, and constructs a three-stage progressive training curriculum from easy to hard samples. Using YOLOv11s as a strong baseline, the proposed hybrid curriculum is compared against conventional training without curriculum learning and against single-source curricula. Results: On the held-out LUNA25 test set, hybrid curriculum learning increases mAP50 from 0.672 to 0.696, mAP50–95 from 0.369 to 0.385, recall from 0.588 to 0.634, and precision from 0.725 to 0.764. Extensive data-efficiency experiments with proportional reductions (1/2, 1/5, 1/10) and fixed training samples (5000–40,000 slices) further confirm consistent gains across limited-data regimes. Conclusions: These results demonstrate that jointly leveraging intrinsic image complexity and optimization-aware feedback provides effective sample scheduling for robust and data-efficient lung nodule detection. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing)
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