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Secure AI for Biomedical Sensing and Imaging Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 2311

Special Issue Editors


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Guest Editor
School of Information Communication and Technology, Griffith, QLD 4215, Australia
Interests: biomedical image computing; machine and deep learning; digital signal processing; privacy and security
School of Information Communication and Technology, Griffith, QLD 4215, Australia
Interests: biometrics; privacy preserving; information forensics; IoT; cybersecurity
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Engineering and Information technology, University of New South Wales Canberra, Northcott Drive, Canberra, ACT 2610, Australia
Interests: biometrics; security; cybersecurity; bio-cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in secure, trustworthy, and robust AI for biomedical sensing and imaging applications. As AI technologies become increasingly embedded in biomedical systems, from diagnostic imaging to physiological signal monitoring, there is a growing need for solutions that ensure data privacy, model robustness, interpretability, and efficiency under real-world conditions. In addition to secure and privacy-preserving techniques such as adversarial defense, federated learning, and differential privacy, we also welcome research on efficient, robust, and generalizable deep learning methods that address the challenges of limited data, domain shift, and deployment on edge devices. The scope includes AI methods applied to medical imaging, wearable or implantable sensors, and other healthcare-related sensing modalities. We invite contributions that offer novel methodologies, practical frameworks, and application-driven insights. Original research articles, comprehensive reviews, and well-validated application studies are all welcome. Topics of interest include, but are not limited to, the following:

  • Advanced AI for biomedical image analysis;
  • Trustworthy multi-modal sensor data fusion;
  • Robust AI under adversarial or noisy inputs;
  • Privacy-preserving AI methods for medical diagnostics;
  • Federated and distributed learning with sensitive health data;
  • Explainable AI for clinical decision-making;
  • Lightweight deep models for edge-based healthcare applications.

Dr. Yanming Zhu
Dr. Xuefei Yin
Prof. Dr. Jiankun Hu
Guest Editors

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Published Papers (2 papers)

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14 pages, 1011 KB  
Article
3D TractFormer: 3D Direct Volumetric White Matter Tract Segmentation with Hybrid Channel-Wise Transformer
by Xiang Gao, Hui Tian, Xuefei Yin and Alan Wee-Chung Liew
Sensors 2026, 26(3), 1068; https://doi.org/10.3390/s26031068 - 6 Feb 2026
Viewed by 527
Abstract
Segmenting white matter tracts in diffusion-weighted magnetic resonance imaging (dMRI) is of vital importance for brain health analysis. It remains a challenging task due to the intersection and overlap of tracts (i.e., multiple tracts coexist in one voxel) and the data complexity of [...] Read more.
Segmenting white matter tracts in diffusion-weighted magnetic resonance imaging (dMRI) is of vital importance for brain health analysis. It remains a challenging task due to the intersection and overlap of tracts (i.e., multiple tracts coexist in one voxel) and the data complexity of dMRI images (e.g., 4D high spatial resolution). Existing methods that demonstrate good performance implement direct volumetric tract segmentation by performing on individual 2D slices. However, this ignores 3D contextual information, requires additional post-processing, and struggles with the boundary handling of 3D volumes. Therefore, in this paper, we propose an efficient 3D direct volumetric segmentation method for segmenting white matter tracts. It has three key innovations. First, we propose to deeply interleave convolutions and transformer blocks into a U-shaped network, which effectively integrates their respective strengths to extract spatial contextual features and global long-distance dependencies for enhanced feature extraction. Second, we propose a novel channel-wise transformer, which integrates depth-wise separable convolution and compressed contextual feature-based channel-wise attention, effectively addressing the memory and computational challenges of 4D computing. Moreover, it helps to model global dependencies of contextual features and ensures each hierarchical layer focuses on complementary features. Third, we propose to train a fully symmetric network with gradually sized volumetric patches, which can solve the challenge of few 3D training samples and further reduce memory and computational costs. Experimental results on the largest publicly available tract-specific tractograms dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods. Full article
(This article belongs to the Special Issue Secure AI for Biomedical Sensing and Imaging Applications)
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26 pages, 461 KB  
Systematic Review
A Systematic Review of Federated and Cloud Computing Approaches for Predicting Mental Health Risks
by Iram Fiaz, Nadia Kanwal and Amro Al-Said Ahmad
Sensors 2026, 26(1), 229; https://doi.org/10.3390/s26010229 - 30 Dec 2025
Cited by 1 | Viewed by 1255
Abstract
Mental health disorders affect large numbers of people worldwide and are a major cause of long-term disability. Digital health technologies such as mobile apps and wearable devices now generate rich behavioural data that could support earlier detection and more personalised care. However, these [...] Read more.
Mental health disorders affect large numbers of people worldwide and are a major cause of long-term disability. Digital health technologies such as mobile apps and wearable devices now generate rich behavioural data that could support earlier detection and more personalised care. However, these data are highly sensitive and distributed across devices and platforms, which makes privacy protection and scalable analysis challenging; federated learning offers a way to train models across devices while keeping raw data local. When combined with edge, fog, or cloud computing, federated learning offers a way to support near-real-time mental health analysis while keeping raw data local. This review screened 1104 records, assessed 31 full-text articles using a five-question quality checklist, and retained 17 empirical studies that achieved a score of at least 7/10 for synthesis. The included studies were compared in terms of their FL and edge/cloud architectures, data sources, privacy and security techniques, and evidence for operation in real-world settings. The synthesis highlights innovative but fragmented progress, with limited work on comorbidity modelling, deployment evaluation, and common benchmarks, and identifies priorities for the development of scalable, practical, and ethically robust FL systems for digital mental health. Full article
(This article belongs to the Special Issue Secure AI for Biomedical Sensing and Imaging Applications)
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