AI-based Virtual Staining
transforming cell and tissue analytics 

VIRTUALSTAIN: special session at ICTS4eHealth 2026, 

in conjunction with the 31st IEEE Symposium on Computers and Communications (ISCC)

23 - 26 June, 2026 - Vilamoura, Algarve, Portugal

About the special session:

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 approaches. 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.

This Special Session, VIRTUALSTAIN, aims to bring together recent advances in AI-driven image and signal based virtual staining field, focusing on methods and systems developed for research, clinical use and R&D. 

Related to ICTS4eHealth 2026 topic areas:

• AI for Precision Medicine

• Knowledge Discovery and Decision Support

• Data/Visual Mining for Diagnostics

• Medical Imaging Analysis

• Computer Vision and Pattern Recognition

• Advanced Medical Visualization

Call for papers:

We invite high-quality submissions on deep learning based virtual staining related to cell, tissue and biomaterial research and use. 

Topics include (but are not limited to):

• Generative Models for Medical Imaging (GANs, Diffusion, Flow Matching)

• Image-to-Image Translation

• Stain-to-stain conversion

• Labelfree microscopy

• Labelfree histology

• Quality metrics, uncertainty and error estimation in virtual staining

• Applications based on various microscopy and spectroscopy measurement techniques,
  including, but not limited to: fluorescence and brightfield microscopy, tomography, photoacoustic, hyperspectral, FTIR, Raman

• Applications on various materials, including, but not limited to: 2D and 3D cell systems, tissue sections, cytology, biomaterials

• Detection and virtual staining based on chemicals, immunostains, probes, particles