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Diagnostics
Special Issues
Recent Advances in AI and Hyperspectral Techniques for Medical Imaging
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Recent Advances in AI and Hyperspectral Techniques for Medical Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 225

Special Issue Editor

Dr. Hsianmin Chen

E-Mail Website
Guest Editor
Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
Interests: medical imaging; hyperspectral medical imaging; digital image processing; biomedical image processing

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and hyperspectral imaging (HSI) are two rapidly evolving technologies that are transforming medical imaging. This Special Issue, titled “Recent Advances in AI and Hyperspectral Techniques for Medical Imaging”, aims to present recent progress in the development and application of AI-based and HSI-based techniques for medical image analysis and clinical diagnosis.

We welcome contributions focusing on AI approaches such as deep learning for image reconstruction, enhancement, segmentation, and classification. Submissions related to hyperspectral imaging applications in tissue characterization, inflammation detection, and surgical guidance are also encouraged. While studies integrating both AI and HSI are of interest, independent advancements in either area are equally valued.

This Special Issue provides a platform for researchers, clinicians, and engineers to exchange insights and promote interdisciplinary innovations in intelligent and spectral medical imaging.

We look forward to your contributions.

Dr. Hsianmin Chen
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 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. 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

  • AI in medical imaging
  • hyperspectral medical imaging
  • biomedical image processing
  • deep learning
  • image segmentation and classification
  • multimodal imaging
  • intelligent diagnostic systems
  • tissue characterization
  • spectral imaging in medicine
  • clinical applications of AI and HSI

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

Published Papers (1 paper)

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Review

48 pages, 2994 KB  
Review
From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?
by Honda Hsu, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Rehan Samirkhan Attar, Ping-Hung Liu and Hsiang-Chen Wang
Diagnostics 2025, 15(21), 2718; https://doi.org/10.3390/diagnostics15212718 (registering DOI) - 27 Oct 2025
Abstract
Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The [...] Read more.
Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article
(This article belongs to the Special Issue Recent Advances in AI and Hyperspectral Techniques for Medical Imaging)
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