Data-Centric and Trustworthy AI for Biomedical Signal and Image Analysis

Dear Colleagues,

Biomedical research and clinical practice are generating increasingly large and heterogeneous datasets, spanning medical images (e.g., CT, MRI, pathology, fundus), physiological signals (e.g., ECG, EEG, PPG) and multimodal records. Despite rapid progress in AI models, real-world deployment is still limited by data-centric challenges: imperfect and expensive annotations, variable acquisition protocols across sites/devices, hidden dataset bias, distribution shift and the need for reliable evaluation, reproducibility and privacy-aware data use.

This Special Issue, “Data-Centric and Trustworthy AI for Biomedical Signal and Image Analysis,” aims to collect recent advances that place data quality, data efficiency and data reliability at the center of algorithm and system design. The topic strongly aligns with Big Data and Cognitive Computing by emphasizing scalable analytics and learning from complex biomedical data, as well as practical concerns such as integrity, robustness and privacy-preserving computation in healthcare settings.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Data curation and quality control (noise, artifacts, missingness, harmonization)
• Annotation-efficient learning (weak supervision, semi/self-supervised learning, active learning)
• Robustness under dataset shift (multi-center generalization, OOD detection, domain adaptation)
• Uncertainty quantification, calibration and reliability assessment (including ground-truth-free QA)
• Bias/fairness analysis, leakage prevention, provenance and reproducible benchmarks
• Privacy-preserving learning and analytics (federated learning, differential privacy, secure computation)
• Deployable pipelines for real-time/streaming signals and clinical translation (edge–cloud workflows)

We look forward to receiving your contributions.

Dr. Yizhe Zhang
Guest Editor

Manuscript Submission Information

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