Plenary Event
Artificial Intelligence Plenary
26 August 2026 • 5:00 PM - 5:45 PM PDT | Conv. Ctr. Room 6A
Session Chair: Giovanni Volpe, Göteborgs Univ. (Sweden)
5:00 PM - 5:05 PM:
Welcome and Opening Remarks
5:05 PM - 5:45 PM:
Artificial intelligence for microscopy: from image analysis to intelligent imaging
Recent advances in microscopy are generating unprecedented volumes of high-resolution biological data across spatial and temporal scales. Artificial intelligence is rapidly transforming how this data is interpreted, enabling the automated extraction of quantitative information from complex images and opening new opportunities for data-driven discovery in biology and medicine.
This lecture will discuss recent progress in the integration of deep learning with microscopy workflows, highlighting how AI methods are reshaping the analysis of biological images and enabling reproducible, scalable computational pipelines. Particular emphasis will be placed on the development of accessible AI tools and open computational frameworks that allow researchers to deploy advanced machine-learning models directly within established bioimaging environments.
Interdisciplinary approaches that combine AI-based image analysis with physical and biological modeling are also emerging as powerful strategies to study complex biological systems. Such approaches enable quantitative investigation of cell mechanics, tissue organization, and large-scale three-dimensional histological reconstruction, illustrating how computational methods can reveal new insights into biological structure and function across scales.
Finally, emerging directions at the intersection of artificial intelligence and optical imaging will be discussed, including smart microscopy systems capable of adaptive acquisition, agent-based AI approaches for scientific discovery, and the growing importance of sustainable computing in data-intensive bioimaging.
Arrate Muñoz Barrutia is Full Professor at Universidad Carlos III de Madrid and Senior Researcher at Instituto de Investigación Sanitaria Gregorio Marañón. She received a PhD from EPFL (Switzerland) and has held visiting positions at Johns Hopkins University and Caltech. Her research focuses on artificial intelligence for quantitative bioimaging, with particular emphasis on deep learning, reproducible bioimage analysis workflows, and multiscale modeling of disease. She has contributed to foundational developments in bioimage segmentation, 3D histological reconstruction, and open-source AI tools that bridge advanced machine learning with experimental microscopy, including deepImageJ and the BioImage Model Zoo. Her work spans computational oncology, neurodegeneration modeling, and data-driven approaches to understand tissue organization and disease progression. She is an IEEE Fellow and serves as Associate Editor of IEEE Transactions on Medical Imaging.
SETUP: Theater style seating.
5:00 PM - 5:05 PM:
Welcome and Opening Remarks
5:05 PM - 5:45 PM:
Artificial intelligence for microscopy: from image analysis to intelligent imaging
 |
Arrate Muñoz-Barrutia
Univ. Carlos III de Madrid (Spain); Instituto de Investigación Sanitaria Gregorio Marañón (Spain) |
Recent advances in microscopy are generating unprecedented volumes of high-resolution biological data across spatial and temporal scales. Artificial intelligence is rapidly transforming how this data is interpreted, enabling the automated extraction of quantitative information from complex images and opening new opportunities for data-driven discovery in biology and medicine.
This lecture will discuss recent progress in the integration of deep learning with microscopy workflows, highlighting how AI methods are reshaping the analysis of biological images and enabling reproducible, scalable computational pipelines. Particular emphasis will be placed on the development of accessible AI tools and open computational frameworks that allow researchers to deploy advanced machine-learning models directly within established bioimaging environments.
Interdisciplinary approaches that combine AI-based image analysis with physical and biological modeling are also emerging as powerful strategies to study complex biological systems. Such approaches enable quantitative investigation of cell mechanics, tissue organization, and large-scale three-dimensional histological reconstruction, illustrating how computational methods can reveal new insights into biological structure and function across scales.
Finally, emerging directions at the intersection of artificial intelligence and optical imaging will be discussed, including smart microscopy systems capable of adaptive acquisition, agent-based AI approaches for scientific discovery, and the growing importance of sustainable computing in data-intensive bioimaging.
Arrate Muñoz Barrutia is Full Professor at Universidad Carlos III de Madrid and Senior Researcher at Instituto de Investigación Sanitaria Gregorio Marañón. She received a PhD from EPFL (Switzerland) and has held visiting positions at Johns Hopkins University and Caltech. Her research focuses on artificial intelligence for quantitative bioimaging, with particular emphasis on deep learning, reproducible bioimage analysis workflows, and multiscale modeling of disease. She has contributed to foundational developments in bioimage segmentation, 3D histological reconstruction, and open-source AI tools that bridge advanced machine learning with experimental microscopy, including deepImageJ and the BioImage Model Zoo. Her work spans computational oncology, neurodegeneration modeling, and data-driven approaches to understand tissue organization and disease progression. She is an IEEE Fellow and serves as Associate Editor of IEEE Transactions on Medical Imaging.
Event Details
FORMAT: General session with live audience Q&A to follow the presentation.SETUP: Theater style seating.