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!

The next call for Helmholtz Imaging Projects is OPEN until July 30, 2025. Find out more about the project call in this summary.

Helmholtz Imaging Projects


Hyperspectral data cube
Image: Aaron Christian Banze

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 Imaging Project cryoFocal, image for overview page
 

cryoFocal

3D Reconstruction from defocused cryo-EM images

This project explores how defocused images recorded with an electron microscope can be used to reconstruct the 3D structure of molecules inside cells. This method aims to enable faster and more cost-effective structural analysis of molecules to accelerate understanding of their functions and to design drugs against them.
Decorative image, HI ImageTox
Image: Jonas Baumann, HIPS

ImageTox

Automated image-based Detection of Early Toxicity Events in Zebrafish Larvae

ImageTox wants to establish an automated image-based system to assess zebrafish larval development. This will allow for a fast and unbiased evaluation of pathophysiological events during toxicological studies. To achieve this, the imaging process has to be optimized and a reliable model for sequence recognition based on deep learning has to be developed.

Helmholtz Foundation Model Initiative (HFMI) Projects


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.
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.

Third-Party Projects


Decorative image
 

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.
Two people standing in a computer center; COMFORT logo is integrated in the image
Image: Tim Roith

COMFORT

COMFORT aims to achieve breakthroughs in developing compact, flexible, and robust machine learning models for image, audio, and network data. In doing so, its application-oriented research program will advance the mathematical understanding of machine learning at the intersection of effectiveness and robustness.
Visual for BestMeta
 

BestMeta

Behavioral Standard Metadata

Developing metadata standards and FAIR analysis pipelines for Video Tracking Assays (VTAs) in toxicology and medical sciences