AI-Based Biomedical Signal and Image Processing

Dear Colleagues,

This Special Issue explores cutting-edge AI technologies for biomedical signal and image processing, addressing critical challenges in healthcare diagnostics and monitoring. Recent advances in deep learning, computer vision, and signal processing algorithms have revolutionized medical data analysis, enabling more accurate disease detection, personalized treatment planning, and real-time patient monitoring. The integration of AI with multimodal biomedical data presents unprecedented opportunities for early diagnosis, treatment optimization, and precision medicine. However, challenges remain in algorithm interpretability, data privacy, clinical validation, and resource-efficient deployment. This Special Issue invites original research addressing novel AI approaches for medical imaging enhancement, disease classification, biosignal interpretation, multimodal fusion, and edge computing implementations. We particularly welcome contributions focusing on explainable AI methods, federated learning for privacy preservation, low-resource applications, and real-world clinical validation studies that demonstrate practical impacts in healthcare settings.

We particularly welcome contributions on topics that include, but are not limited to, the following:

• Deep Learning Architectures for Medical Image Analysis
  Research on novel neural network architectures specifically designed for medical imaging tasks, including segmentation, classification, and anomaly detection across modalities such as MRI, CT, ultrasound, and histopathology.
• Explainable AI Methods for Healthcare Applications
  Studies focusing on interpretable and transparent AI systems that provide clinically meaningful explanations for their predictions, enabling trust and facilitating regulatory approval for clinical deployment.
• Multimodal Fusion Techniques for Integrated Diagnosis
  Work on algorithms that combine information from multiple imaging modalities and clinical data sources to improve diagnostic accuracy and provide more comprehensive patient assessment.
• Federated Learning for Privacy-Preserved Medical Data Analysis
  Research on collaborative AI training paradigms that maintain data privacy by enabling model training across institutions without sharing sensitive patient data.
• Low-Resource and Edge Computing Solutions for Healthcare
  Innovations in deploying AI models on resource-constrained devices for point-of-care diagnostics, remote monitoring, and applications in low-resource settings.
• AI-Enhanced Biosignal Processing for Continuous Monitoring
  Approaches to the real-time analysis of biosignals (ECG, EEG, EMG, etc.) using AI to enable the early detection of adverse events and continuous patient monitoring.
• Synthetic Data Generation and Data Augmentation for Medical Applications
  Methods to address data scarcity in medical domains through the generation of realistic synthetic data and advanced augmentation techniques that preserve clinical relevance.
• Few-Shot and Self-Supervised Learning in Medical Imaging
  Research on learning paradigms that reduce dependency on large labelled datasets, enabling effective model training with limited annotations.
• AI for Treatment Planning and Response Prediction
  Applications of AI in personalizing treatment strategies and for predicting patient outcomes based on imaging and clinical data.
• Clinical Validation and Implementation Studies
  Research demonstrating the practical impact of AI solutions in real clinical workflows, including validation studies, deployment challenges, and economic evaluations.

Dr. Bhanu Prakash KN
Guest Editor

Manuscript Submission Information

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• deep learning
• medical imaging
• biosignal processing
• explainable AI
• computer-aided diagnosis
• edge computing
• multimodal fusion
• federated learning
• clinical validation
• precision medicine