Pixel Pandemonium at ECR 2026
The Pixel Pandemonium at ECR 2026 is the perfect platform to explore up-and-coming AI and ML tools for medical imaging. This program offers a unique opportunity to see innovative technology first-hand and get a hands-on experience!
Discover the latest AI/ML technology in the medical imaging sphere. We want to see what is coming and what might have an impact in the next couple of years!
The Pixel Pandemonium Exhibition is endorsed by the Medical Image Computing and Computer Assisted Intervention Society (MICCAI).
Demos in the exhibition include 15 software tools showcasing cutting-edge AI technology.
Come and find us at the Level -2, Foyer K
Aya Elgebaly (Copenhagen; Denmark)
An interactive AI tool for discovering and visualizing bias in fetal growth prediction models
Philipp Arnold (Freiburg; Germany)
We turn free-style radiology dictation into accurate, structured, and template-aligned reports.
Charles Kahn (Philadelphia; United States)
ATLAS catalogs AI models and datasets with standardized ontology cards, indexed by subspecialty, RadLex terms, and keywords, and accessible via web and API.
Eleftherios Tzanis (Heraklion; Greece)
mAIstro is an autonomous, open-source multi-agent system designed to orchestrate the full pipeline of medical imaging AI development – from exploratory data analysis and radiomics feature extraction to training and deploying deep learning models.
Miriam Groeneveld (Nijmegen; Netherlands)
Grand Challenge is an online platform to run biomedical image analysis challenges, hosting data and submissions with automated evaluation, reader studies, and transparent benchmarking to support reproducible medical AI validation.
Oleksander Berezovskyi (Odesa; Ukraine)
An adaptable workflow integrating MacWhisper and local LLMs like MedGemma (or remote LLMs on openrouter.ai) to seamlessly transform dictated audio into structured, clinically-accurate radiology reports.
Benito Farina (Madrid; Spain)
This demo estimates lung nodule malignancy from up to three longitudinal CT scans, quantifying temporal progression and providing interpretable, flexible analysis with partial inputs.
Daniel Capellán-Martín (Madrid; Spain)
Discover Hope4kids, our three-time-winning AI tool that automates high-accuracy segmentation for a diverse range of brain tumors, including pediatric cases, to accelerate clinical decision-making.
Mihaela Hedesiu (Cluj-Napoca; Romania)
DentiHUB connects digital workflows, advanced imaging, and AI to shape the future of personalized dentistry.
David Montalvo-Garcia (Madrid; Spain)
A deep learning-based algorithm for the identification and quantification of fibrotic and hypoattenuation patterns in chest CT, supporting improved clinical understanding of pulmonary fibrosis.
Marcio Aloisio Bezerra Cavalcanti Rockenbach (Somerville; United States)
AI Arena enables interactive clinical evaluation of AI in real-world use cases: users review cases, assess report drafts from radiologists or AI in a blinded workflow, then explore provenance and voting analytics.
Joshy Cyriac (Basel; Switzerland)
Empowering users to transform AI results and clinical data into report-ready text with increased speed and improved quality.
Luc Builtjes (Nijmegen; Netherlands)
LLM Extractinator – A practical toolkit for extracting structured medical data from text using large language models.
Dimitrios Bounias (Heidelberg; Germany)
Turning routine breast MRI into a fully-automated cardiovascular health check: PACS integrated AI detects incidental findings (e.g., aortic aneurysms, pulmonary artery dilation, cardiomegaly) and delivers clear structured reports without disrupting radiologists’ workflows.
Francesco Di Feola (Umeå; Sweden)
DeepTwin-X showcases a GenAI-enabled radiological Digital Twin for virtual scanning, demonstrating CT-to-PET, MRI-to-CT, and CBCT-to-CT translation.