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Symmetry
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Hybrid Deep Learning and Explainable AI for Symmetry-Aware and Multiscale...
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Hybrid Deep Learning and Explainable AI for Symmetry-Aware and Multiscale Medical Image Analysis

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 355

Special Issue Editors

Prof. Dr. Leandro Alves Neves

E-Mail Website
Guest Editor
Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil
Interests: pattern recognition; machine learning; image processing; computer graphics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcelo Zanchetta do Nascimento

E-Mail Website
Guest Editor
Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Uberlândia 38400-902, Brazil
Interests: medical imaging; machine learning; computational intelligence; digital image processing; biological systems modeling; hybrid deep learning; explainable AI (XAI); semantic segmentation; multiscale and multidimensional approaches
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lucas C. Ribas

E-Mail Website
Guest Editor
Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil
Interests: machine learning; data science; complex systems; network science; computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Wallace Casaca

E-Mail Website
Guest Editor
Institute of Mathematical and Computational Sciences (ICMC), São Paulo State University (UNESP), São José do Rio Preto 15054-000, SP, Brazil
Interests: machine learning; data science; remote sensing; computer vision; time series analysis

Special Issue Information

Dear Colleagues,

This Special Issue will advance research on Hybrid Deep Learning and Explainable Artificial Intelligence (XAI) for medical diagnostic imaging, with particular emphasis on symmetry-aware modeling and multiscale computational representations. We welcome contributions that integrate Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and multiscale, multidimensional, or multiresolution feature extractors to improve classification, segmentation, and anomaly detection tasks. We also encourage studies exploring model-enrichment strategies that incorporate geometric or anatomical symmetries, mirror-aware attention mechanisms, multilevel feature fusion, explainability-driven constraints, or complex network-based descriptors to enhance model performance, robustness, and clinical interpretability. Submissions addressing theoretical formulations, algorithmic innovation, benchmarking frameworks, or real-world clinical deployments will be highly appreciated. Both methodological advances and analytical reviews demonstrating clear scientific novelty, mathematical rigor, and reproducibility are well aligned with the aims of this Special Issue.

Prof. Dr. Leandro Alves Neves
Prof. Dr. Marcelo Zanchetta do Nascimento
Prof. Dr. Lucas C. Ribas
Dr. Wallace Casaca
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. 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

  • hybrid deep learning
  • explainable AI (XAI)
  • symmetry-aware modeling
  • model-enrichment strategies
  • vision transformers (ViTs)
  • convolutional neural networks (CNNs)
  • multiscale, multidimensional, and multiresolution descriptors
  • complex-network-based descriptors
  • medical image analysis
  • geometric and structural explainability
  • computer-aided diagnosis (CAD)

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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

26 pages, 2603 KB  
Article
Perceiving Symmetry and Variability: A Probabilistic Vision–Language Framework for Medical Image Segmentation
by Jiu Jiang, Qi Zhou and Chu He
Symmetry 2026, 18(5), 859; https://doi.org/10.3390/sym18050859 (registering DOI) - 19 May 2026
Viewed by 55
Abstract
Medical image segmentation is challenging due to subtle pathological patterns and the inherent ambiguity of clinical descriptions. Although vision–language models have shown promise, they frequently lack fine-grained perception of structural variability. To address these limitations, we propose the Symmetry- and Variability-Perceiving Conditional Variational [...] Read more.
Medical image segmentation is challenging due to subtle pathological patterns and the inherent ambiguity of clinical descriptions. Although vision–language models have shown promise, they frequently lack fine-grained perception of structural variability. To address these limitations, we propose the Symmetry- and Variability-Perceiving Conditional Variational Autoencoder (SVP-CVAE). The proposed method integrates a clinical attribute encoder with a morphology-aware enhancement module that incorporates a cross-bilateral symmetry mechanism to explicitly capture symmetry-related variations. By reformulating the segmentation task as a probabilistic prior-to-posterior inference process, SVP-CVAE models the one-to-many mapping between textual attributes and visual realizations. Furthermore, we introduce an attribute-latent contrastive objective to ensure that the latent space encodes discriminative morphological information. Extensive experiments demonstrate that the proposed framework achieves superior segmentation accuracy compared to state-of-the-art methods. Results indicate that SVP-CVAE effectively captures diverse yet anatomically plausible structural variations while maintaining high sensitivity to bilateral symmetry. Comprehensive ablation studies confirm that the performance gains are synergistically driven by the proposed symmetry-perceiving module and the contrastive semantic alignment objective, rather than relying solely on the probabilistic formulation. In conclusion, integrating explicit symmetry perception with probabilistic modeling significantly enhances the reliability and interpretability of multimodal medical image segmentation in complex clinical scenarios. Full article
(This article belongs to the Special Issue Hybrid Deep Learning and Explainable AI for Symmetry-Aware and Multiscale Medical Image Analysis)
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