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


 

SyNaToSe

Leveraging Cross-Domain Synergies for Efficient Machine Learning of Nanoscale Tomogram Segmentation

The aim is to develop an adaptable algorithm that can be used to perform different tasks in data and image analysis without needing to be trained with new, laboriously annotated images for each separate task.
Helmholtz Imaging Project FOMIA, brain
Image: FOMIA

FOMIA

A Foundation Model for Microscopy Image Analysis

This project will develop a foundation model trained on a large and diverse dataset of microscopy images to facilitate the adaptation of artificial intelligence methods to biological image analysis.
Decorative image explaining WeMonitor
Image: WeMonitor

WeMonitor

Satellite-based Earth observation to detect natural hazards

Satellite imagery makes it possible to detect spatio-temporal anomalies on the Earth's surface, including natural hazards such as landslides, deforestation, or the emergence of large waste dump sites. This project aims to use artificial intelligence to detect these changes at an early stage and to be able to monitor their progress.

Helmholtz Foundation Model Initiative (HFMI) Projects


Image: NicoElNino on Shutterstock

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.
Microcosmos of the Ocean by Klas Ove Möller, Hereon
Image: NicoElNino on Shutterstock

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.
decorative image
Image: NicoElNino on Shutterstock

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


Manual microscopic biodiversity monitoring is time consuming and expert requiring thus limits the potential for biodiversity monitoring and hence to recognize risks climate and environmental changes on biodiversity related to crucial ecosystem functions.
Image: AIMBIS

AIMBIS – Artificial Intelligence for Microscopic Biodiversity Screening

Manual microscopic biodiversity monitoring is time-consuming and requires expert knowledge, limiting the potential for biodiversity monitoring, hence to recognize the impacts of climate and environmental change on crucial ecosystem functions.
Visusal for SCHEMA;Metastases represent a significant exacerbation of tumor severity. If one could predict the likelihood of tumors metastasizing, this could inform treatment decisions to avoid or delay this outcome. SCHEMA develops a benchmark dataset of primary tumor samples and metadata on whether the tumor has metastasized at different time points after sampling. With this dataset, a challenge for machine learning scientists will be defined to build prognostic models for likelihood of tumors metastasizing, promoting innovation in prognostic modeling for a clinically relevant task.
Image: Hellmut Augustin, DKFZ (BSIC 2021 contribution)

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.
Visual for GRIDMARK; 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.
 

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


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SFB Transregio 154 – C06: Transport metrics for analysis and optimization of network problems

SFB TRR 154 is a project of the German Research Foundation (DFG) and combines integer-continuous methods, model adaptation, and numerical simulation, to analyze and optimize gas markets, infrastructure, and control of networks. The third funding period specifically focuses on the transition from natural gas to hydrogen.
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Foundations of Supervised Deep Learning for Inverse Problems

Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.
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Bayesian Computations for Large-scale (Nonlinear) Inverse Problems in Imaging

During research stays with the collaborating group at Caltech, we aim to investigate various aspects of statistical inverse problems. This includes inquiries into particle- and PDE-based sampling methods, as well as robust regularization using neural networks.