Diagnosis of Medical Imaging

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 979

Special Issue Editors


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Guest Editor
Department of Computer Engineering & Informatics, University of Patras, 26504 Patras, Greece
Interests: medical imaging; deep learning; breast cancer diagnosis; robotics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical image processing; breast cancer detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical imaging; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the years, medical imaging techniques such X-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET) have revolutionized the way we diagnose and treat various medical conditions. These imaging modalities provide detailed insights into the human body, allowing healthcare professionals to identify diseases, monitor treatment progress, and guide surgical interventions.The aim of this Special Issue is to present the recent advances in medical imaging for detection and diagnosis, including through the use of machine learning and deep learning algorithms.

We especially invite submissions that utilize various Medical Imaging modalities such as digital mammography (DM), tomosynthesis, ultrasound, or MRI, to develop systems that assist in the diagnosis (CADx) and/or detection (CADe) of regions of interest in diseases. Submissions may also include, but are not limited to, innovative feature extraction techniques for disease detection and diagnosis, transfer learning and deep learning architectures, open-access databases for breast cancer research, generative adversarial network (GAN) architectures designed to address the challenges of small datasets.

The goal of this Special Issue is to explore our current standing and future possibilities within this crucial area of health-related research. We welcome submissions detailing new techniques, methods, applications, and results, as well as review articles.

Dr. Athanasios Koutras
Dr. Dermatas Evangelos
Dr. Ioanna Christoyianni
Dr. George Apostolopoulos
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. 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.

Published Papers (1 paper)

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Research

12 pages, 3132 KiB  
Article
Intraoperative PRO Score Assessment of Actinic Keratosis with FCF Fast Green-Enhanced Ex Vivo Confocal Microscopy
by Daniela Hartmann, Lisa Buttgereit, Lara Stärr, Elke Christina Sattler, Lars Einar French and Maximilian Deußing
Appl. Sci. 2024, 14(3), 1150; https://doi.org/10.3390/app14031150 - 30 Jan 2024
Viewed by 798
Abstract
Actinic keratoses (AKs) represent a common skin cancer in situ associated with chronic sun exposure. Early diagnosis and management of AKs are crucial to prevent their progression to invasive squamous cell carcinoma. Therefore, we investigated AK PRO score assessment using ex vivo confocal [...] Read more.
Actinic keratoses (AKs) represent a common skin cancer in situ associated with chronic sun exposure. Early diagnosis and management of AKs are crucial to prevent their progression to invasive squamous cell carcinoma. Therefore, we investigated AK PRO score assessment using ex vivo confocal laser microscopy (EVCM) coupled with a novel fluorescent dye, FCF Fast Green, to explore its potential for the precise imaging and discrimination of collagen fibers. AK PRO assessment using EVCM demonstrated excellent conformity (95.8%) with histopathologic examination. The additional utilization of FCF Fast Green dye had no impact on AK visualization but showed a high affinity for collagen fibers enabling clear differentiation of collagen alterations between healthy and sun-damaged skin. The enhanced visualization of collagen fiber changes may aid clinicians in accurately identifying AKs and differentiating them from benign skin lesions. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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