Accepted Special Sessions:

SS-??: AI-based Virtual staining transforming cell and tissue analytics

Examination of tissues and cells forms the basis of disease diagnostics and prognostics in pathology for major disease groups such as cancer. Tissue sections and cells are nearly invisible to the naked eye, but specific stainings make the structures, morphology and biomarkers visible through various chemical reactions, results of which are observed through microscopy. These processes are laborious, sample- and chemical-consuming, and used in high volumes globally. AI-based technologies enable development virtual staining mehods, the production of cell and tissue stainings computationally by deep learning-based methods. These entail image-to-image translation-based methods with generative neural network models learning stain-to-stain or unstained-to-stained translations. A variety of imaging modalities, target material and stains, and a plethora of computational approaches can be employed. The topic is significant due to these novel methods holding tremendous potential in this particular application area with domain-specific challenges. Especially, decreasing number of laboratory assays required per patient, directly contributing to diagnostic delivery times, and decreasing costs for the healthcare system are amongst the expected future impacts.

TOPICS (but are not limited to):

• AI for Precision Medicine
• Knowledge Discovery and Decision Support
• Data/Visual Mining for Diagnostics
• Medical Imaging Analysis
• Generative Models for Medical Imaging (GANs, Di usion, Flow Matching)
• Image-to-Image Translation
• Computer Vision and Pattern Recognition
• Advanced Medical Visualization

co-Chairs:

• Dr. Leena Latonen, University of Eastern Finland, Kuopio, Finland
• Prof. Pekka Ruusuvuori, University of Turku, Finland

web-site:TBA
Submission Link:TBA

SS-GAIN-Health: Generative AI for Next-generation e-Health

Generative Artificial Intelligence offers impressive opportunities in the next generation e-health systems, having the potential to accelerate clinical research, improve diagnostic accuracy through advanced multimodal analysis, support rare disease studies via high-quality synthetic data augmentation, and significantly reduce the administrative burden on clinicians by automating repetitive and time-consuming tasks. The GAIN-Health Special Session aims to provide a focused forum for researchers, practitioners, and industry stakeholders to present advances, share insights, and discuss the challenges and opportunities of applying generative AI to the e-Health ecosystems. The session welcomes innovative models and novel methodologies, real-world applications, and critical perspectives on the responsible use of GenAI in medicine.

TOPICS (but are not limited to):

• Synthetic medical image generation and 3D data augmentation
• Biosignal generation and enhancement (ECG, EEG, EMG, PPG, etc.)
• Digital twins, virtual patients, and simulation frameworks
• Large Language Models and Clinical NLP
• Automated medical report generation
• Conversational agents and LLM-based clinical assistants
• Multimodal integration of text, imaging, bio signal etc
• Cross-modal generation (e.g., text-to-image for medical diagnostics)
• Multimodal reasoning for clinical decision support
• Use cases, Benchmarks, and Real-World Deployment

co-Chairs:

• Prof. Mario Vento, University of Salerno, Italy
• Prof. Alessia Saggese, University of Salerno, Italy
• Dr. Martino Giaquinto, University of Salerno, Italy
• Mr. Simone Bove, University of Salerno, Italy
• Mr. Antonio Vitale, University of Salerno, Italy

web-site: https://mivia.unisa.it/gain2026/
Submission Link: https://edas.info/N35011

SS-INSIGHT: INtelligent Systems for Imaging-based diaGnosis in HealThcare

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.

TOPICS (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

co-Chairs:

• Dr. Roberta De Fazio, University of Campania Luigi Vanvitelli, Italy
• Dr. Ciro Russo, University of Cassino and Lazio Meridionale, Italy
• Mrs. Grazia Gargano, University of Bari Aldo Moro, Italy
• Mr. Gaetano Settembre, University of Bari Aldo Moro, Italy

web-site: https://gaetanosettembre.github.io/insight-icts4ehealth-2026/
Submission Link: https://edas.info/N35012

SS-NCMD: Non-Contact Medical Diagnostics

Measurement of vital stats of the human body such as heart rate, blood pressure, electrocardiogram (ECG), etc is an important part of diagnosing and detecting medical conditions. Conventional methods to measure body parameters involve specialized sensors which need to be placed on the body. Non-contact diagnostics using human speech, facial/body imaging and radar sensors has garnered significant research interest as it can minimize the spread of infection and also allows remote, real-time monitoring of individuals. By applying signal processing and developing appropriate AI models, low-cost and timely healthcare solutions can be designed for early diagnosis of illnesses and tracking the progress of treatment/recovery. Non-invasive, non-contact diagnostics in healthcare can greatly improve the effectiveness and efficiency of healthcare service providers.

TOPICS (but are not limited to):

• Vital stats (heart rate, SpO2, BP) monitoring from speech using AI
• Mental health monitoring using speech
• Cognitive and motor analysis through audio and video for neurological diseases monitoring
• Physiological monitoring using patient’s pose estimation and facial expression recognition (FER) analysis
• Non-contact Athletes’ monitoring in sport medicine
• Speech/image/video/radar - sensed data signal-processing using AI for medical diagnosis
• AI based assisted living
• Radar based healthcare monitoring
• Information fusion involving multimodal health datasets (audio, video, and radar sensed data)

co-Chairs:

• Dr. Mohammed Usman, Bennett University, India
• Dr. Vijayalakshmi Ahanathapillai, University of Warwick, United Kingdom
• Dr. Mohammad Wajid, Aligarh Muslim University, India

web-site: https://sites.google.com/view/ieee-icts4ehealth-2026/home
Submission Link:https://edas.info/N35013

SS-AIMAXR-TR: AI-Based Motion Analysis and Extended Reality for Telemedicine and Rehabilitation

Recent advances in computer vision, artificial intelligence, markerless motion capture, and extended reality (XR) systems are transforming remote healthcare, from rehabilitation to telepresence-enabled surgery and medical training. This session welcomes technical and clinical contributions exploring XR combined with AI-driven movement analysis for telemedicine, telerobotics, and adaptive rehabilitation. Topics include real-time pose estimation and depth sensing, multimodal sensor fusion, closed-loop serious games, automated motor scoring, outcome prediction, and XR-supported operating theatre scenarios for training and remote assistance. By bridging engineering innovation with clinical validation, the session highlights scalable, evidence-based workflows that enable personalized care, immersive telepresence, and next-generation medical education across real-world clinical environments.

TOPICS (but are not limited to):

• AI-based motion analysis for rehabilitation and telemedicine
• Markerless motion capture and pose estimation in clinical environments
• Depth sensing and multimodal sensor fusion for movement monitoring
• XR (VR/AR/MR) systems for telerehabilitation and remote assessment
• Serious games and gamified XR with closed-loop feedback for motor recovery
• Automated motor scoring, functional outcome prediction, and clinical decision support
• Cloud–edge AI architectures and IoMT-based infrastructures for telehealth and rehabilitation
• Telerobotics and XR interfaces for telepresence, tele-mentoring, and remote intervention
• Clinical trials and real-world studies of AI/XR-based rehabilitation systems
• Usability, patient engagement, trust, privacy, and ethical aspects of AI/XR in telemedicine

co-Chairs:

• Mr. Giovanni Lonia, University of Messina, Italy
• Mrs. Mirjam Bonanno, University of Messina and IRCCS Centro Neurolesi Bonino-Pulejo, Italy
• Prof. Rocco Salvatore Calabrò, IRCCS Centro Neurolesi Bonino-Pulejo, Italy
• Prof. Salvatore Livatino, University of Hertfordshire, United Kingdom

web-site: https://giannilonix.github.io/AIMAXR-TR-icts4ehealth/
Submission Link: https://edas.info/N35031