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.
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Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
3DforestSIF
Understanding the solar-induced fluorescence (SIF) signal of natural, complex tree canopies
3DforestSIF seeks to correct airborne solar-induced fluorescence (SIF) data from forests for canopy structural and illumination effects, providing valuable insights for the early detection of forest stress.
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.
PlastoView
Microplastic Detection with the PlastiScope
Water quality is essential for ecosystems and human health, yet it's increasingly threatened by microplastics. This project develops image-based methods for detecting both plankton and microplastics using a new low-cost, mobile system.Helmholtz Foundation Model Initiative (HFMI) Projects
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.
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.UNLOCK – Benchmarking Projects
BASE: Benchmarking Agro-environmental database for Sustainable agriculture intensification
Building a BASE dataset enables robust predictions of yield potential, resource efficiency, and sustainability thresholds, driving climate resilience and sustainable agricultural intensification
AMOEBE: lArge-scale Multi-mOdal Microbial livE-cell imaging BEnchmark
Building a large-scale, FAIR benchmark for AI-driven analysis of microbial communities using time-lapse microscopy to advance understanding of microbial dynamics, ecosystem stability, and their role in health and biotechnology.
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.Third-Party Projects
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.
BestMeta
Behavioral Standard Metadata
Developing metadata standards and FAIR analysis pipelines for Video Tracking Assays (VTAs) in toxicology and medical sciences