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Electronics
Special Issues
AI-Driven Medical Image/Video Processing
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AI-Driven Medical Image/Video Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 122

Special Issue Editor

Prof. Dr. Jitae Shin

E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea
Interests: deep learning algorithm; image/video signal processing; medical image processing and system; image/video communication and systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative force in the field of medical image and video processing, offering unprecedented opportunities to improve diagnosis, treatment planning, and patient outcomes. As AI-driven image processing continues to shape modern technology, addressing challenges, such as those of computational efficiency, interpretability, and ethical considerations, remains crucial. Additionally, there is a need to foster interdisciplinary collaboration and knowledge exchange between clinical professionals and AI experts, bringing more accurate diagnosis and treatment through an integrated approach to the disease. This special issue aims to explore the latest theoretical advancements, methodologies, and practical applications of AI in medical imaging and video analysis. We invite contributions that highlight innovative uses of AI-driven techniques to address challenges in medical image interpretation, video analysis, multimodal imaging, and clinical decision support. Through this issue, we seek to foster a deeper understanding of AI's role in revolutionizing healthcare and its potential to enhance the precision, efficiency, and accessibility of medical practices worldwide.

Prof. Dr. Jitae Shin
Guest Editor

Manuscript Submission Information

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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. Electronics 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.

Keywords

  • AI techniques in medical imaging
  • medical image/video processing
  • multimodal imaging
  • diagnostic imaging
  • data augmentation and synthetic data
  • explainable AI in clinical decision support
  • deep learning for image/video analysis
  • medical image and video enhancement
  • applications of large language models (LLMs) in medical domain
  • AI-driven smart devices for medical image/video
  • clinical decision support

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Published Papers (1 paper)

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Research

22 pages, 2133 KiB  
Article
Classification of Whole-Slide Pathology Images Based on State Space Models and Graph Neural Networks
by Feng Ding, Chengfei Cai, Jun Li, Mingxin Liu, Yiping Jiao, Zhengcan Wu and Jun Xu
Electronics 2025, 14(10), 2056; https://doi.org/10.3390/electronics14102056 - 19 May 2025
Viewed by 14
Abstract
Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address [...] Read more.
Whole-slide images (WSIs) pose significant analytical challenges due to their large data scale and complexity. Multiple instance learning (MIL) has emerged as an effective solution for WSI classification, but existing frameworks often lack flexibility in feature integration and underutilize sequential information. To address these limitations, this work proposes a novel MIL framework: Dynamic Graph and State Space Model-Based MIL (DG-SSM-MIL). DG-SSM-MIL combines graph neural networks and selective state space models, leveraging the former’s ability to extract local and spatial features and the latter’s advantage in comprehensively understanding long-sequence instances. This enhances the model’s performance in diverse instance classification, improves its capability to handle long-sequence data, and increases the precision and scalability of feature fusion. We propose the Dynamic Graph and State Space Model (DynGraph-SSM) module, which aggregates local and spatial information of image patches through directed graphs and learns global feature representations using the Mamba model. Additionally, the directed graph structure alleviates the unidirectional scanning limitation of Mamba and enhances its ability to process pathological images with dispersed lesion distributions. DG-SSM-MIL demonstrates superior performance in classification tasks compared to other models. We validate the effectiveness of the proposed method on features extracted from two pretrained models across four public medical image datasets: BRACS, TCGA-NSCLC, TCGA-RCC, and CAMELYON16. Experimental results demonstrate that DG-SSM-MIL consistently outperforms existing MIL methods across four public datasets. For example, when using ResNet-50 features, our model achieves the highest AUCs of 0.936, 0.785, 0.879, and 0.957 on TCGA-NSCLC, BRACS, CAMELYON16, and TCGA-RCC, respectively. Similarly, with UNI features, DG-SSM-MIL reaches AUCs of 0.968, 0.846, 0.993, and 0.990, surpassing all baselines. These results confirm the effectiveness and generalizability of our approach in diverse WSI classification tasks. Full article
(This article belongs to the Special Issue AI-Driven Medical Image/Video Processing)
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