AI and Intelligent Sensors for Medical Imaging
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 9
Special Issue Editors
Interests: image processing; computer vision; image forensics
Interests: sample information assessment; AI and object detection
Interests: vision; AI; autonomous and human centred systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Anomaly detection in medical imaging faces significant challenges, including small lesion sizes, high morphological diversity, and the difficulty in interpreting complex medical data. While Artificial Intelligence (AI) technologies play a pivotal role in medical image analysis, the limitations of prevailing AI paradigms become increasingly apparent when confronting complex clinical scenarios. For instance, their decision-making processes often lack clinical interpretability, failing to provide a transparent rationale for physicians' diagnoses and thus undermining the clinical trust in their outputs. Furthermore, model development heavily relies on large-scale annotated data, which stands in stark contrast to the real-world challenges of the scarcity and inaccessibility of medical data, thereby severely compromising their generalization ability and robustness. Concurrently, the mainstream research paradigm, which typically focuses on single sources of information, is misaligned with the practical clinical workflow that necessitates decision-making based on the synthesis of multi-source information. The core of this Special Issue is to systematically address the core scientific challenges of interpretability, unsupervised/weakly supervised learning, domain generalization, and multi-modal fusion. Its significance lies in overcoming the application bottlenecks of current AI technologies and providing genuine support for clinical decision-making. Therefore, we encourage submissions that place a strong emphasis on how their proposed methods address the clinical realities, clearly articulating their contributions to enhancing model trustworthiness, robustness, and data efficiency.
Scope:
- Explainable AI and Clinical Trust
- Data-Efficient Learning Paradigms
- Model Robustness: Domain Adaptation and Generalization
- Multi-modal Fusion for Precision Diagnosis
- Fine-grained Analysis: Detection, Segmentation and Quantification
- AI for Intelligent Sensors and Novel Imaging
- Generative AI in Medical Imaging
- Privacy-Preserving and Collaborative Learning
- Longitudinal Analysis and Disease Progression Modeling
Dr. Shuai Xiao
Dr. Zhuo Zhang
Prof. Dr. Qinggang Meng
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- anomaly detection
- medical imaging
- deep learning
- computer-aided diagnosis
- object detection
- intelligent sensors
- unsupervised learning
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