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Symmetry
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Symmetric/Asymmetric Study in Medical Imaging
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Symmetric/Asymmetric Study in Medical Imaging

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1249

Special Issue Editor

Dr. Xi Zhang

E-Mail Website
Guest Editor
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Interests: nuclear images; PET/SPECT images; image reconstruction

Special Issue Information

Dear Colleagues,

This Special Issue titled "Symmetric/Asymmetric Study in Medical Imaging" focuses on the fundamental and applied aspects of symmetry and asymmetry in medical imaging. We are pleased to invite you to contribute to this Special Issue.

This Special Issue aims to bring together novel research that leverages symmetric and asymmetric properties for improved image processing, machine learning, and deep learning approaches in clinical imaging. We welcome submissions addressing theoretical models, algorithm development, and clinical applications that demonstrate the importance of symmetry or asymmetry in medical imaging workflows.

Symmetry plays a pivotal role in medical image analysis, especially in anatomical structures like the brain, lungs, and breasts, where bilateral symmetry is often expected. Deviations from symmetry—whether due to disease, injury, or anatomical variability—can serve as early indicators of pathology. Asymmetric analysis is increasingly being used to improve diagnostic accuracy in neuroimaging, oncology, and organ segmentation. Advanced algorithms are now capable of detecting subtle asymmetries in PET, MRI, and CT scans, aiding in the diagnosis of conditions such as Alzheimer’s disease, stroke, and tumors. At the same time, symmetric constraints are widely employed in image registration, reconstruction, and enhancement to maintain structural consistency and improve data fidelity.

I look forward to receiving your contributions.

Dr. Xi Zhang
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. Symmetry is an international peer-reviewed open access monthly 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 processing
  • neuroimaging
  • brain asymmetry
  • image registration
  • image reconstruction
  • MRI
  • PET/SPECT imaging
  • CT imaging
  • diagnostic imaging
  • deep learning in medical imaging

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

Published Papers (1 paper)

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Research

22 pages, 1280 KB  
Article
Enhancing Early Skin Cancer Detection: A Deep Learning Approach with Multi-Scale Feature Refinement and Fusion
by Siyuan Wu, Pengfei Zhao, Huafu Xu and Zimin Wang
Symmetry 2026, 18(4), 612; https://doi.org/10.3390/sym18040612 - 5 Apr 2026
Viewed by 551
Abstract
The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations in skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and effective [...] Read more.
The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations in skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and effective treatment. However, achieving precise and efficient segmentation remains a major challenge, as existing methods often struggle to capture complex lesion characteristics. To address this challenge, we propose a novel deep learning framework that integrates the PVT v2 backbone with two key modules: the Spatial-Aware Feature Enhancement (SAFE) module and the Multiscale Dual Cross-attention Fusion (MDCF) module. The SAFE module enhances multi-scale encoder features through a dual-branch architecture, which adaptively extracts offset information to integrate fine-grained shallow details with deep semantic information, thereby bridging the feature gap across network depths. The MDCF module establishes bidirectional cross-attention between decoder and encoder features, followed by multi-scale deformable convolutions that capture lesion boundaries and small fragments across heterogeneous receptive fields, thereby enriching semantic details while suppressing background interference. The proposed model was evaluated on two public benchmark datasets (ISIC 2016 and ISIC 2018), achieving Intersection over Union (IoU) scores of 87.33% and 83.67%, respectively. These results demonstrate superior performance compared to current state-of-the-art methods and indicate that our framework significantly enhances skin lesion image analysis, offering a promising tool for improving early detection of skin cancer. Full article
(This article belongs to the Special Issue Symmetric/Asymmetric Study in Medical Imaging)
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