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Deep Learning for Advanced Visual Representation and Analysis
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Deep Learning for Advanced Visual Representation and Analysis

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 March 2027 | Viewed by 496

Special Issue Editor

Prof. Dr. Guangyao Li

E-Mail Website
Guest Editor
College of Computer Science and Technology, Tongji University, Shanghai 201804, China
Interests: visual representation learning; multimodal information fusion; robust computer vision; domain generalization; advanced pattern recognition

Special Issue Information

Dear Colleagues,

The rapid evolution of deep learning has revolutionized the field of computer vision, enabling unprecedented advancements in how visual data is represented and analyzed. This Special Issue, titled "Deep Learning for Advanced Visual Representation and Analysis," aims to gather cutting-edge research that addresses the challenges of extracting meaningful information from complex visual environments.

We invite original research and review articles focusing on novel neural network architectures, representation learning, and their practical applications. In particular, we focus on techniques that improve model robustness and generalizability, such as domain adaptation and domain generalization, which are crucial for real-world scenarios. Furthermore, we welcome contributions in specialized domains like medical imaging analysis, where advanced visual representation is essential for accurate segmentation and diagnosis.

We invite original research articles and reviews covering research areas that may include (but are not limited to) the following:

  • Novel deep learning architectures for visual computing;
  • Self-supervised and unsupervised representation learning;
  • Domain adaptation and domain generalization in computer vision;
  • Advanced medical image segmentation and multi-modal analysis;
  • Cognitive-driven visual representation models;
  • Lightweight neural networks for efficient visual analysis.

I look forward to your contributions.

Prof. Dr. Guangyao Li
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. Big Data and Cognitive Computing 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 1800 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

  • deep learning
  • visual representation
  • computer vision
  • domain generalization
  • neural network architecture
  • image feature extraction
  • multimodal information fusion

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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

23 pages, 2131 KB  
Article
MDA-Net: A Segmentation Network for Kidney Tumor Based on Enhanced Multi-Scale Feature Extraction and Attention Refinement
by Shaofu Lin, Yumiao Chang, Jianhui Chen and Lianfang Ma
Big Data Cogn. Comput. 2026, 10(5), 149; https://doi.org/10.3390/bdcc10050149 - 8 May 2026
Viewed by 246
Abstract
Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still [...] Read more.
Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still struggle with multi-scale tumor characteristics, complex morphological variations, and background noise in medical images. To address these challenges, we propose MDA-Net, an end-to-end segmentation method based on enhanced multi-scale feature extraction and attention refinement. Specifically, we introduce a Multi-Scale Feature Extraction (MSFE) module into encoder–decoder skip connections to aggregate dilated features across multiple receptive fields and learn branch-wise weights for adaptive refinement and fusion, thereby enhancing boundary details and semantic cues to reduce tumor-tissue ambiguity. At the bottleneck, a Deformable Pyramid Feature Refinement (DPFR) module combines deformable sampling with pyramid contextual modeling, thereby improving adaptability to variations in tumor shape and scale while preserving feature resolution. Moreover, a Channel and Spatial Attention (CASA) module is embedded in the decoder to suppress background interference and enhance boundary-sensitive structures during upsampling via coordinated channel and spatial reweighting, thereby improving the reconstruction of fine-grained tumor morphology and contours. Experiments on both KiTS19 and KiTS21 show that MDA-Net consistently improves tumor boundary delineation, lesion localization, and mask reconstruction, demonstrating stronger robustness and cross-dataset generalizability than representative baseline methods. Ablation studies further confirm the complementary effects of MSFE, DPFR, and CASA. In addition, Grad-CAM visualizations improve the clinical transparency and interpretability of the model. Overall, this method advances deep learning for medical image analysis and supports precise diagnosis and treatment of renal tumors. Full article
(This article belongs to the Special Issue Deep Learning for Advanced Visual Representation and Analysis)
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