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Mathematics
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Advances in Computer Vision and Image Processing with Applications to...
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Advances in Computer Vision and Image Processing with Applications to Bioinformatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 156

Special Issue Editor

Prof. Dr. Zhe Liu

E-Mail Website
Guest Editor
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
Interests: medical image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Recent advances in computer vision and image processing have significantly expanded the potential of bioinformatics. Over the past few years, this field has seen significant breakthroughs due to the rapid development of deep learning, which has enabled more precise, automated, and large-scale analysis of complex bioinformatics data.

This Special Issue intends to provide a forum for researchers developing and reviewing new methods and applications in computer vision and image processing with a focus on bioinformatics. We are pleased to invite you to submit original research articles, comprehensive reviews, novel theoretical contributions, or practical applications that advance the state of the art in bioinformatics, including, but not limited to, deep learning for bioinformatics, semi-supervised learning for biomedical data, computer vision for disease diagnostics, biomedical image analysis, and multi-modal data fusion.

I look forward to receiving your contributions.

Prof. Dr. Zhe Liu
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. Mathematics 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

  • bioinformatics
  • deep learning
  • multi-modal learning
  • disease diagnostics
  • biomedical image analysis

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Published Papers (1 paper)

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Research

20 pages, 3823 KB  
Article
DA-TransResUNet: Residual U-Net Liver Segmentation Model Integrating Dual Attention of Spatial and Channel with Transformer
by Kunzhan Wang, Xinyue Lu, Jing Li and Yang Lu
Mathematics 2026, 14(3), 575; https://doi.org/10.3390/math14030575 - 5 Feb 2026
Abstract
Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they still face challenges in complex medical imaging scenarios, particularly around simultaneously modeling fine-grained [...] Read more.
Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they still face challenges in complex medical imaging scenarios, particularly around simultaneously modeling fine-grained local details and capturing long-range global contextual information, which limits segmentation accuracy and structural consistency. To address these challenges, this paper proposes a novel medical image segmentation framework termed DA-TransResUNet. Built upon a ResUNet backbone, the proposed network integrates residual learning, Transformer-based encoding, and a dual-attention (DA) mechanism in a unified manner. Residual blocks facilitate stable optimization and progressive feature refinement in deep networks, while the Transformer module effectively models long-range dependencies to enhance global context representation. Meanwhile, the proposed DA-Block jointly exploits local and global features as well as spatial and channel-wise dependencies, leading to more discriminative feature representations. Furthermore, embedding DA-Blocks into both the feature embedding stage and skip connections strengthens information interaction between the encoder and decoder, thereby improving overall segmentation performance. Experimental results on the LiTS2017 dataset and Sliver07 dataset demonstrate that the proposed method achieves incremental improvement in liver segmentation. In particular, on the LiTS2017 dataset, DA-TransResUNet achieves a Dice score of 97.39%, a VOE of 5.08%, and an RVD of −0.74%, validating its effectiveness for liver segmentation. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Image Processing with Applications to Bioinformatics)
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