NEW SUBMISSION DEADLINE: 31 March 2026!!!
Submission platform (EDAS link) for submission: https://edas.info/N35012

INSIGHT: INtelligent Systems for Imaging-based diaGnosis in HealThcare @ IEEE ICTS4eHealth 2026

INSIGHT focuses on intelligent systems and AI-driven methods for imaging-based diagnosis in healthcare, bridging robust machine learning, interpretability, and clinically actionable decision support.

Intelligent Systems are playing an increasingly important role in clinical practice, supporting physicians and healthcare professionals in disease diagnosis, therapy optimization, and health monitoring. Among these advancements, a promising research direction is imaging-based diagnosis, where intelligent systems enable accurate, robust, and timely interpretation of medical images.

This Special Session, INSIGHT, aims to bring together recent advances in AI-driven image analysis for healthcare, focusing on methods and systems that support imaging-based clinical decision making across key application domains (e.g., radiology, digital and computational pathology, computational cytology, and neuroimaging). Contributions addressing both methodological advances and clinically oriented applications are encouraged.

We invite high-quality submissions on intelligent systems and machine learning methods for imaging-based diagnosis in healthcare. Topics include (but are not limited to):
- Imaging-based clinical decision support system for patient-specific outcome prediction
- AI-driven medical image analysis across imaging modalities (e.g., CT, MRI, PET, ultrasound)
- AI-based systems in digital pathology and computational cytology
- Multimodal medical image analysis and data integration
- Learning-based medical image segmentation, classification, and anomaly detection
- Generative and diffusion-based models for medical imaging, including synthetic data generation
- Self-supervised and unsupervised learning for medical image analysis
- Explainable and interpretable AI for medical imaging and neural signal analysis, including post-hoc and ante-hoc approaches
- Human-AI collaboration for explainable and clinically usable systems
- Federated learning and privacy-preserving approaches for medical imaging
- Bias, fairness, and safety in AI-driven medical imaging systems
- Clinical validation, reproducibility, and regulatory aspects of AI for medical imaging
- Novel datasets, benchmarks, and evaluation methodologies for AI in medical imaging
- Real-world deployment and clinical impact of AI-driven medical imaging systems
- Ethical, legal, and societal implications of AI in medical imaging
- Data governance, quality, and standardization in medical imaging AI

Paper format:
Papers should follow the IEEE conference format and the ICTS4eHealth submission rules (up to 7 pages; accepted papers up to 6 pages without extra charge, per conference info). Please use the IEEE Conference Templates.