SCHEMA develops AI-Ready Dataset to Predict Cancer Metastasis. Project leads: Dr. Malte Lücken and Prof. Markus Diefenbacher, both LHI
SCHEMA brings together Helmholtz Munich and partner institutions to create the largest single-cell, spatial dataset for predicting metastasis in lung, colon, and breast cancer. By combining public data with newly generated tumor profiles, the project provides a benchmark for AI scientists to develop models that forecast which tumors are likely to spread. SCHEMA aims to accelerate discovery of predictive biomarkers, guide personalized treatment, and ultimately support new strategies for tackling cancer metastasis.
Given the complexity of the disease, says Malte Lücken, cooperation is crucial: “One isolated laboratory will not be able to provide groundbreaking insights. This is why we have curated a team, encompassing AI specialists, molecular biologists, clinicians, toxicologists and geneticists, located throughout Germany and spanning multiple Helmholtz Centers and additional partner sites.”
Markus Diefenbacher adds that the vision of SCHEMA is to “shed new light on the pathophysiological mechanism of solid tumour metastasis, a clinical challenge with currently limited biomarker availability. Not only could this spark new research topics within the realm of metastatic research but would bear fruit with regard to initiation of drug development and small molecule design via the available world leading units available at Helmholtz.”
Learn more about SCHEMA!: helmholtz-imaging.de/project/schema/
UQOB Develops Multi-Rater Benchmark for Reliable Object Detection in Organoid Imaging. Project Lead: Dr. Marie Piraud (Helmholtz AI)
UQOB develops the first benchmark dataset for object detection and uncertainty quantification in microscopy images of alveolar and bronchiolar organoids from mouse and human lungs, complemented by colon organoid data. The dataset will include over 800 high-resolution images and 120,000 annotated organoids.
For UQOB, the LHI group leaders Mareike Lehmann and Maja Funk will image, profile and characterize organoids from various organs, including the lung. With AI they want to uncover biological phenotypes and make our work faster and more efficient:
“Our aim is also to publish the images as a resource for everyone which promises to be the biggest dataset of organoid images there is so far! We hope that beyond helping our research this will be useful for many researchers around the world!”
Learn more about UQOB!: helmholtz-imaging.de/project/uqob/
Each project will receive up to €150,000 from the Helmholtz Initiative and Networking Fund. The research teams will collaborate closely with Helmholtz AI and Helmholtz Imaging to establish best practices in metadata management, reproducible workflows, and open-access datasets. Starting in January 2026, the projects will take part in workshops to share knowledge and foster the emerging AI benchmarking community. Together, these projects demonstrate Helmholtz Munich’s contribution to shaping the next generation of trustworthy AI across scientific domains.