RI-Scale - Making bioimaging data discoverable, accessible, and understandable with AI

About the webinar

This talk explores two approaches to making bioimaging data discoverable, accessible, and understandable through AI and RI-Scale’s Data Exploration Platforms. First, we discuss training a foundation model on a large cryo-electron tomography (cryo-ET) dataset to support image categorisation, interpretation, and quantitative analysis. We then explore how AI-powered assistants can help users navigate the BioImage Archive, find suitable tools, and perform tasks such as cell counting and segmentation, making data exploration and analysis accessible to researchers with varying levels of technical expertise.

Target Audience

• BioImaging community, particularly scientsists developing large AI models that would like to use the DEP for training. Also CryoET scientists that could be interested in the foundational model, and general BioImage Archive users that could be interested on the chatbot

Agenda

• SUC7 presentation
• SUC8 presentation
• Q&A

About the speakers

Teresa Zulueta-Coarasa is the Technical Coordination Project Leader at EMBL-EBI’s BioImage Archive. Her work focuses on enabling the reuse of large-scale bioimaging datasets by improving their discoverability, standardisation, and interoperability, supporting data-driven research and AI applications in the life sciences. She coordinates the development of the BioImage Archive’s web platform and supporting infrastructure, helping make complex microscopy datasets easier to explore and visualise online without requiring users to download large volumes of data.

Joanna Hård holds a PhD from Karolinska Institutet and was a Swiss National Science Foundation postdoctoral fellow at ETH Zurich, where Joanna developed computational and probabilistic methods to reconstruct cell lineages from single-cell DNA and RNA sequencing data. Joanna is now a postdoctoral researcher in the AICell Lab at KTH and SciLifeLab. Joanna’s work focuses on developing AI-powered tools for analysing sensitive and large-scale biomedical data.