Contributed papers are solicited describing original works in Intelligent Medicine and Image Computing. Topics and technical areas of interest include but are not limited to the following:

 

Track 1: Artificial Intelligence & Intelligent Medicine
Machine Learning and Deep Learning in Healthcare: Novel algorithms for disease diagnosis, prognosis, and therapy planning; Clinical Decision Support Systems: Rule-based, statistical, and AI-driven systems for real-world clinical settings; Natural Language Processing (NLP) in Medicine: Mining electronic health records (EHR), clinical notes, and biomedical literature; Large Language Models (LLMs) for Medical Applications: Development, fine-tuning, and evaluation of domain-specific models; Predictive Modeling and Risk Analysis: AI for patient outcome prediction, readmission rates, and personalized risk scores; Robotics and Automation in Surgery: Intelligent control systems, human-robot interaction, and surgical simulation; Drug Discovery and Development: AI applications in molecular generation, protein structure prediction, and virtual screening; Wearable Technology and IoT for Health Monitoring: Smart sensors, real-time data analysis, and remote patient management;
Track 2: Medical Image Computing & Signal Processing
Medical Image Segmentation: Semantic/instance segmentation of tumors, organs, and anatomical structures (CT, MRI, Ultrasound); Image Registration and Fusion: Multi-modal image alignment (e.g., PET/CT) and atlas-based methods; Image Reconstruction and Enhancement: Novel techniques for denoising, super-resolution, and artifact reduction; Computer-Aided Diagnosis (CAD) and Detection: Automated detection of abnormalities (lesions, nodules, microcalcifications) ; Radiomics and Quantitative Imaging: Extraction and analysis of high-dimensional imaging features ; Biomedical Signal Processing: Analysis of ECG, EEG, EMG signals for diagnostic support. Image-Guided Interventions and Therapy: Real-time imaging for surgical navigation and interventional radiology ;
Track 3: Cross-disciplinary & Emerging Technologies
Explainable AI (XAI) in Medicine: Interpretability and transparency of "black-box" models for clinical trust; Federated Learning and Data Privacy: Privacy-preserving techniques for multi-institutional medical data analysis ; Generative Models in Medicine: Synthetic data generation (GANs, VAEs, Diffusion Models) for data augmentation and anonymization ; Multimodal Data Fusion: Integrating imaging data with genomics (radiogenomics), proteomics, and clinical records ; Digital Twins in Healthcare: Virtual replicas of physiological systems or individual patients for simulation ; Computational Pathology and Genomics: Digital pathology analysis and AI for genomic sequence interpretation;
Track 4: Clinical Applications & Translational Research
AI in Neurology and Neuroscience: Brain image analysis, Alzheimer's prediction, stroke assessment; AI in Cardiology: Cardiac image analysis, risk stratification from echocardiograms, arrhythmia detection ; AI in Oncology: Tumor characterization, treatment response assessment, and radiotherapy planning ; AI in Ophthalmology: Retinal image analysis for diabetic retinopathy and glaucoma detection ; Pandemic Preparedness and Infectious Disease Modeling: AI applications in epidemiology and public health ; Healthcare Systems and Public Health Informatics: Population health management, health policy modeling ;