Month: November 2023

Introducing the BioImage.IO Chatbot, a game-changer for the bioimage analysis community. This cutting-edge AI-driven assistant is revolutionizing how biologists, bioimage analysts, and developers interact with advanced tools. The BioImage.IO Chatbot excels in delivering personalized responses, code generation, and execution, as demonstrated by various usage examples.

The BioImage.IO Chatbot draws from diverse sources, including databases like ELIXIR bio.tools, documentation from different tools and softwares such as deepImageJ or ImJoy, and the BioImage Model Zoo documentation, ensuring tailored, context-aware answers. The result? Personalized responses that cater to users’ unique requirements.

Distinguished by its versatility, the chatbot adeptly handles both simple and technical queries, ensuring that it remains a valuable asset to users of all backgrounds. What’s more, we are enthusiastic about fostering a community-driven ecosystem. We encourage individuals to integrate their documentation and data sources into our knowledge base, thereby enriching the experience for everyone.

As we prepare for the upcoming beta testing phase (sign up here), join us in witnessing how the BioImage.IO Chatbot is reshaping the landscape of bioimage analysis. Be a part of this transformative journey!

For a detailed overview, check out our preprint.

After a highly successful 2nd General Assembly in Heidelberg, AI4Life took advantage of the gathered members and organized a Hackathon & Solvathon. The event was held from October 10th to 13th, 2023, with a total of 25 participants.

Two parallel events ran during this period:

• Hackathon: Focused on building and deploying AI tools for bioimage analysis, enhancing scalability and FAIRness. Participants engaged in discussions, formed interest groups around projects, and collaborated on various web & cloud bioimage analysis topics.
  The Hackathon members participated in a number of projects:
  • EDAM & bio.tools collaboration with AI4Life
  • Segment Anything in the BioEngine
  • Segment Anything in the BioImage Model Zoo
  • bioimageio Python packages
  • bioimageio uploader
  • ChatBot for the BioImage Model Zoo
  • Documentation improvement
  • Kubernetes BioEngine on Google Cloud
  • Semantic Segmentation of cartilaginous tissue
• Solvathon: Experts from AI4Life and beyond worked together on the 8 selected open-call projects. Detailed progress updates on these projects will be shared soon.

Both tracks achieved remarkable outcomes. Stay tuned for the next event!

A new video has been released through our YouTube channel. We asked the participants of the recent Hackathon & Solvathon for a word that describes AI4Life. Hear what they said below!

The AI4Life project held its 2nd General Assembly on October 9th and 10th, 2023, bringing together computational experts and life scientists from various fields to discuss the project’s progress and future goals. 

The assembly opened with a welcome from the Euro-BioImaging director, John Eriksson and Euro-BioImaging Bio-Hub director, Antje Keppler.

Anna Kreshuk and Florian Jug, the AI4Life Scientific Coordinators, discussed AI4Life’s objectives, including democratizing AI-based methods, establishing standards for submission, storage, and FAIR (Findable, Accessible, Interoperable, and Reusable) data and models. They also highlighted the need for open calls and empowering common platforms with AI integration. 

The keynote speaker was Gergely Sipos, who presented “Computing and AI in EOSC”, discussing opportunities for collaboration. Sipos emphasized the role of EGI, an international e-infrastructure for research and innovation, and its mission to provide computing power, data storage, and training services to the scientific community. He also introduced iMagine, another Horizon Europe-funded project that makes heavy use of EGI resources. Together, AI4Life, iMagine, and EGI decided to explore ways to team up and re-use each others’ technology stacks.

The rest of the meeting was structured from two different angles:

1) Updates from partners: describing the interaction with each other, analysing dependencies within the project, summarising the activities during the first year, and the outlook for the one to come;

2) Updates from Work Packages: describing the goals, deliverables and milestones (submitted and upcoming), and the interactions between work packages. Special focus was given to the question of whether needs have changed since the grant for AI4Life was written. Work Packages also updated the audience with success stories, achievements, goal blockers, pain points, and challenges.

We would like to thank all the participants for their contributions and we are looking forward to an exciting second year of FAIR AI with AI4Life!

Artificial Intelligence (AI) methods have revolutionised the analysis of biological images, but their performance depends on the data the models are trained with. Therefore, to develop, benchmark, and reproduce the results of AI methods, developers need access to high-quality annotated data.

One of the missions of AI4Life is to democratise the access to well-annotated datasets which are standardised to facilitate their reuse, and presented in a manner that is useful to the community. As part of this effort the BioImage Archive has launched a gallery of datasets that can be explored in-browser without the need to download the images and annotations. Each dataset is presented in a consistent way, following community metadata standards that include information such as the biological application of a dataset, what type of annotations a dataset contains, the licence the data are under or what models have been trained using this dataset. Furthermore, because all images are converted from different formats into the cloud-ready file format OME-Zarr, there is potential for analysing these datasets in the cloud. 

The BioImage Archive team plans to keep enriching this collection with more datasets over time, with the aim of establishing a community resource that can empower the development of new AI methods for biological image analysis.