Projects
With our Helmholtz Imaging Projects, Helmholtz Foundation Model Initiative (HFMI) and third-party funded projects, we aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.
Helmholtz Imaging offers a funding line of Helmholtz Imaging Projects, striving to seed collaborations between centers and across research fields. They are a strong incentive to enable interdisciplinary collaboration across the Helmholtz Association and an incubator and accelerator of the Helmholtz Imaging network.
In addition to our Helmholtz Imaging Projects, the Helmholtz Imaging team has secured external funding for third-party projects contributing their knowledge and expertise on cutting-edge imaging methodology.
Join us in unlocking the limitless potential of Helmholtz Imaging!
Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
POINTR
Mapping Boreal Forest Change Using 3D Radar and Point Cloud Data
Global warming is reshaping northern boreal forests. This project maps forest structure and ecosystem services using high-resolution radar satellite monitoring combined with 3D imaging data.
CLARITY
CineMR-guided ML-driven Breathing Models for Adaptive Radiotherapy
Dose-escalated radiotherapy of lung cancers requires precise monitoring of lesions and nearby organs at risk. Current methods are able to track ultra-central lesions but neglect their deforming vicinity, risking unacceptable toxicity to aortico-pulmonary structures. AI-based anomaly detection and generative AI models can address both requirements in real-time.
HYPER-AMPLIFAI
Advancing Visual Foundation Models for Multi-/Hyperspectral Image Analysis in Agriculture/Forestry
The project aims to make advanced AI models accessible for Hyperspectral Earth Observation, reducing computational demands, and improving environmental assessments through user-friendly interfaces.Helmholtz Foundation Model Initiative (HFMI) Projects
AqQua
AqQua aims to build the first foundational pelagic imaging model using billions of aquatic images worldwide. These images, spanning species from plankton, will help an AI classify species, extract traits, and estimate carbon content, offering key insights into biodiversity, ecosystem health, and the biological carbon pump's role in climate regulation.
The Human Radiome Project (THRP)
The Human Radiome Project (THRP) aims to drive a paradigm shift in medical research, providing novel insights into human health and disease through the power of AI. By integrating diverse radiological data, it seeks to enable groundbreaking advancements in personalized medicine, enhancing diagnostic accuracy and improving patient care.
Synergy Unit
The Synergy Unit amplifies the Helmholtz Foundation Model Initiative's impact by developing AI principles for diverse fields. Collaborating with HFMI projects, it focuses on knowledge sharing, community building, and representation to ensure the initiative's lasting influence.UNLOCK – Benchmarking Projects
SCHEMA – profiling Spatial Cancer HEterogeneity across modalities to benchmark Metastasis risk prediction
SCHEMA creates a benchmark dataset linking tumor samples with metastasis outcomes to enable machine-learning models that predict metastasis risk and support clinical decision-making.
Pero – Unlocking ML Potential: Benchmark Datasets on Perovskite Thin Film Processing
Addressing the lack of standardized, FAIR benchmark datasets in perovskite photovoltaics. Pero enables reproducible AI models for efficiency prediction, material classification, and defect detection, which are critical for industrial scaling of sustainable energy technologies.
GRIDMARK – Generating Reproducible Insights through Data Benchmarking for AI in Energy Systems
Transforming energy systems toward climate neutrality: Distribution grids have the potential to be catalysts for the energy transition. Unfortunately, most Distribution System Operators lack the resources to fully monitor their systems. Therefore, there is an urgent need for more high-quality data, particularly to develop and test machine learning models.Third-Party Projects
UMDISTO: Unsupervised Model Discovery
The project aims to develop novel methods for unsupervised multi-matching to map cellular-level correspondences in organisms like C. elegans.
Spatio-temporal inverse approaches for EEG/MEG reconstruction of neural networks in the human brain
This project aims to develop novel methods for reconstructing brain activity from dynamic EEG and MEG measurements. By using realistic, individualized finite element models and advanced regularization techniques, including machine learning, we seek to solve this inverse problem in real patient settings, ultimately improving the diagnosis and treatment of medication-resistant focal epilepsy.