Speaker
Description
Bioimage analysis is essential for advancing our understanding of cellular processes, yet traditional methods often fall short in scalability and efficiency. To address these challenges, our research focuses on developing a comprehensive infrastructure integrating data streaming and collaborative annotation for training large foundation models. Our streaming dataloader efficiently manages public datasets on AWS S3, enabling decentralized storage and rapid data access through a unified API. This setup supports the development of advanced bioimaging tools, including a collaborative annotation platform that utilizes the Segment Anything Model (SAM) with a human-in-the-loop approach to enhance dataset quality through crowd-sourced annotations. The platform offers flexible deployment options to ensure data privacy. By combining these technologies, we facilitate the development of advanced automated imaging systems and whole-cell modeling, leveraging AI to simulate cellular behaviors and processes. Our goal is to enhance automated microscopy and create a comprehensive whole-cell simulator, driving transformative insights into cellular research and in-silico drug screening.
| Authors | Nils Mechtel*, Estibaliz Gómez de Mariscal, Constantin Pape, Wei Ouyang |
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| Keywords | Collaborative Annotation, Interactive Annotation, Microscopy Image Analysis, Bioimaging, Human-in-the-Loop, Foundation Models, Segment Anything Model, AI4Life, Hypha, BioEngine, Kaibu, Streaming Dataloader, AWS S3, Data Infrastructure |
