X-BRAIN
Cross-modality representation learning for brain analysis and data integration
Multimodal histological imaging allows for the detailed analysis of structural and molecular organization within the human brain, forming a critical foundation for studying brain function and neurodegenerative processes. By integrating data from various imaging modalities at different scales, multimodal brain atlases offer a comprehensive tool for neuroimaging, modeling, and clinical applications, supporting a holistic understanding of brain relationships.
Integrating image data across different subjects and modalities remains a significant challenge in developing fully integrated atlas frameworks. To overcome this, X-BRAIN will develop AI algorithms for learning joint feature representations from multimodal histological images. These algorithms will enable the identification and matching of semantically corresponding images, regardless of their modality. The resulting features will enable data-driven spatial anchoring and multimodal analysis of organizational principles.
Other projects
AIOrganoid
Artificial Intelligence Assisted-Imaging for Creating High-yield, High-fidelity Human Lung Organoid
AIOrganoid will apply cutting-edge imaging techniques and develop novel AI-based solutions to facilitate human lung organoid formation with high yield and fidelity, bridging the gap between cell biology and computational imaging.
MultiSaT4SLOWS
Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service
In order to detect impending landslides before they occur and to enable reliable emergency mapping after a landslide, the researchers are combining optical data with radar data from satellites. Using machine learning methods, computers will be trained to recognise the tiniest of changes in things like sloping landscape surfaces.
UTILE
Autonomous image analysis to accelerate energy materials discovery and integration
Research into green materials for clean energy generation is moving at full speed – yet still requires a long time to complete. This project is working on an open source image processing application that uses artificial intelligence to drive the analysis and management of image data from experiments across the energy materials community.