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!
by Beatriz Serrano-Solano
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!
by Teresa Zulueta-Coarasa
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.
by Jeremy Metz, Beatriz Serrano-Solano and Wei Ouyang
In a major step toward democratizing AI in life sciences, the AI4Life consortium announces the launch of BioEngine—a scalable, cloud-based infrastructure that powers the BioImage Model Zoo. Designed to be accessible to both experts and novices, BioEngine aims to revolutionize the way bioimage analysis is conducted.
The escalating growth of data in life sciences has revealed the limitations of conventional desktop applications used for bioimage analysis. These local solutions are increasingly inadequate to handle high-throughput data and sophisticated applications like AI-driven image analysis. Users often face challenges with large data sets, complex hardware requirements, and intricate software dependencies that make these tools cumbersome to use and difficult to deploy. Additionally, existing machine learning model zoos often necessitate a level of expertise in programming and model selection, making them inaccessible to a wider audience. All these challenges collectively point to the need for a more efficient, scalable, and user-friendly solution for AI model serving.
Enter BioEngine, a state-of-the-art cloud infrastructure designed to simplify the complex landscape of bioimage analysis. BioEngine powers the BioImage Model Zoo, allowing users to test-run pre-trained AI models on their own images without requiring any local installation. Its cloud-based approach means you can easily connect BioEngine to existing software platforms like Fiji, Icy, and napari, thereby eliminating the need to install multiple dependencies.
For the developers in our community, BioEngine serves as a cloud platform designed to cater to many users while judiciously using limited GPU resources. It features a simple API that can be accessed via HTTP or WebSocket, offering a seamless experience for running models in the cloud. This API can be effortlessly integrated into Python scripts, Jupyter notebooks, or web-based applications.
By employing BioEngine, both experts and non-experts can overcome the challenges associated with traditional desktop-based solutions and enter an era of streamlined, accessible and scalable bioimage analysis.
Efficiently serving a variety of models on limited GPU resources required the development of a unique framework that can manage dynamic model execution and scheduling.
BioEngine is the result of a concerted collaboration primarily between KTH Royal Institute of Technology and the BioImage Archive Team at EMBL-EBI, under the auspices of the AI4Life consortium. The platform is built as an extension of the Hypha framework, a robust RPC-based communication hub that orchestrates containerized components for a seamless and efficient operation.
BioEngine provides both HTTP and WebSocket-based RPC APIs. Developers can effortlessly integrate these APIs into web and desktop apps, thereby streamlining the interaction between software and the cloud-based AI models.
We are actively working on broadening our deployment offerings:
With the support the EU Horizon research infrastructure grant, we are committed to promoting accessibility and lowering barriers to advanced bioimage analysis:
We encourage everyone to try BioEngine and provide feedback (via image.sc forum, GitHub issues, or our contact form). Your input is crucial for the continual refinement of this revolutionary platform.
BioEngine offers an easy-to-use API for running models, simplifying software design and deployment.
This API can be integrated into Python scripts, Jupyter notebooks, or web-based applications, giving you the flexibility to adapt BioEngine to your own projects.
We are actively working on an easily deployable package for institutional use and aim to improve stability and scalability.
For more information, please refer to our API documentation.
With BioEngine, we are democratizing advanced bioimage analysis by offering cloud-based, plug-and-play AI solutions that can be effortlessly integrated into existing software ecosystems, thereby accelerating both research and real-world applications in life sciences.
AI4Life is delighted to introduce a brand-new poster, now available on Zenodo.
Discover the AI4Life journey, its goals, structure and opportunities in this informative poster. This resource is freely accessible and open for anyone to use. Download it, share it, print it, and let the knowledge flow!
Happy exploring!
by Caterina Fuster-Barceló
We’re excited to introduce a brand-new addition to the AI4Life project website that will make your experience even more enriching and informative. We’ve just launched the AI4Life Help Desk, a comprehensive resource hub designed to cater to all your queries, support needs, and information requirements.
What is the AI4Life Help Desk?
The AI4Life Help Desk is your go-to destination for accessing a wealth of resources that will enhance your understanding of the AI4Life project and the BioImage Model Zoo. Whether you’re a seasoned user or just getting started, our Help Desk is here to assist you every step of the way.
What’s inside the Help Desk?
Discover the AI4Life Help Desk today at https://ai4life.eurobioimaging.eu/help!
We are excited to introduce a fresh addition to the AI4Life website dedicated to showcasing our project’s accomplishments in a more accessible manner.
Milestones and Deliverables
AI4Life is a very prolific project, we will publish 25 deliverables and 16 milestones throughout the project’s duration. This newly launched section serves as a hub where you can dig into the specifics of each deliverable and milestone. An interactive timeline provides insight into the projected completion dates of our deliverables, offering a clear roadmap for our ongoing endeavours.
Highlighting Our Scientific Contributions
In this same section, we’ve also gathered our scientific publications. By centralizing these contributions, our aim is to foster knowledge-sharing and encourage collaboration within the wider scientific community.
Your Window into AI4Life’s Advancements
We invite you to explore this new section of our website to gain insight into the work that has defined AI4Life’s journey thus far. By making our outputs easily accessible, we hope to engage you with our work. And, for those that want to stay closely connected to AI4Life, we offer the option to subscribe to our newsletter.
AI4Life and Leica are announcing their collaboration to make deep-learning models developed by the bioimage community available to a wider user community through integration into Leica’s AIVIA software.
In a commitment to the scientific community, AI4Life and Leica Microsystems join forces to help researchers to leverage AI in complex experiments. AI4Life, coordinated by Euro-BioImaging, is a Horizon Europe-funded project that brings together the computational and life science communities. Its goal is to empower life science researchers to harness the full potential of Artificial Intelligence (AI) methods for bioimage analysis – and in particular microscopy image analysis, by providing services, and developing standards aimed at both developers and users.
One of the consortium’s objectives is to build an open, accessible, community-driven repository (the BioImage Model Zoo) of FAIR pre-trained AI models and develop services to deliver these models to life scientists. Together with their community partners, the consortium ensures that models and tools are interoperable with Fiji, ImageJ, Ilastik and other open-source software tools.
In the meantime, Leica Microsystems has cultivated a valuable connection with the AI4Life project and the BioImage Model Zoo. Most recently, the Leica team had the opportunity to meet the people behind the AI4Life project at a workshop organized by the Euro-BioImaging Industry Board (EBIB). It was there, through their participation, that they realized the power of their combined resources.
Widely recognized for optical precision and innovative technology, Leica Microsystems supports the imaging needs of the scientific community with AIVIA, their advanced Al-powered image analysis software. AIVIA is a complete 2-to-5D image visualization and analysis platform designed to allow researchers to unlock insights previously out of reach. Through the joined effort, BioImage Model Zoo models can now also be easily integrated into Leica’s commercial software AIVIA.
“We see this initiative as an opportunity to tie in AIVIA with the scientific community and to make work done by the community accessible in a convenient way to AIVIA users.” said Constantin Kappel, Manager AI Microscopy and Insights at Leica Microsystems.
To achieve this, Leica is taking the models that are published in BioImage.io and converting them to their own AIVIA Model repository format for interoperability with AIVIA. From a small number of models, the offer will gradually be increased.
This further supports the aim of AI4Life to lower the barriers for using pre-trained models in image data analysis for users without substantial computational expertise or those with existing licences, who might rely on commercial solutions.
This is a great example of how resources curated by academia and available in open access can be of interest to the imaging industry. The source of the models are recognized directly in AIVIA, a nice testimony to industry-academia collaboration and a nice endorsement of the models curated by the BioImage Model Zoo.
“We much appreciate the interest of Leica. Models relevant to AIVIA will be directly available to users, which is a testimony to fruitful industry-academia collaboration and a great endorsement of the utility of the BioImage Model Zoo. We hope many other software tools will follow Leica’s lead and also start benefitting from this community resource we are currently building” said Florian Jug, one of the scientific coordinators of AI4Life.
“Bioimage.io is supported by AI4Life. AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970.”
More information:
https://www.aivia-software.com/post/pancreatic-phase-contrast-cell-segmentation-bioimage-io
https://www.aivia-software.com/post/hpa-cell-segmentation-bioimage-io
https://www.aivia-software.com/post/hpa-nucleus-segmentation-bioimage-io
https://www.aivia-software.com/post/b-subtilist-bacteria-segmentation-bioimage-io
by Florian Jug & Beatriz Serrano-Solano
The first AI4Life Open Call received an impressive response, with a total of seventy-two applications. It proved to be an incredible opportunity for both life scientists seeking image analysis support and computational scientists eager to explore the evolving landscape of AI methodologies. In this blog post, we announce the awarded projects and invite you to join us behind the scenes as we explore the selection process that determined which projects have been selected.
First things first, here is the list of titles of the selected projects (in alphabetical order):
The projects are diverse, covering scientific topics ranging from Plant Biology, Physiology, Metabolism, Cell Biology, Molecular Biology, Marine Biology, Flow Cytometry, Medical Biology, Regenerative Biology, Neuroscience, etc. The researchers who have proposed the projects come from the following countries: France (2x), Germany, Italy, Netherlands, Portugal, and the USA (2x).
The selection procedure started with internal eligibility checks. Is the project submitted completely? Is the information complete and telling a complete story that is fit for external reviews? At this stage, we only had to drop 10 projects of a grant total of 72 submitted projects. Our intention was to only filter projects that drew an incomplete picture and leave the judgement of the scientific aspects to our reviewers.
After assembling a panel of 16 international reviewers (see list below), we distributed anonymized projects among them. All personal and institutional information was removed, only leaving project-relevant data to be reviewed. We aimed at receiving 3 independent reviews per project, requiring each review to review about 11 projects total.
Here is the list of questions we asked our reviewers via an electronic form:
Due to the unforeseen unavailability of some reviewers, we ended up with about 2.7 reviews per project, with some projects receiving 2 but most projects receiving all 3 desired reviews.
We first aggregated all reviews per project by averaging numerical values and concatenating textual evaluations. We then developed three project scores: a quality score (main metric), a total effort score, and a slightly more subjective excitingness score.
The final score was computed by: 0.75*(quality/effort) + 0.25*excitingness
This formula favors projects that are estimated to be conducted in less time, which is in line with our aim to help more individuals through the AI4Life Open Calls.
The selected projects will be assigned to our AI4Life experts waiting to support them. All other projects are offered a space in the AI4Life Bartering Corner, a new section soon to appear on our website, where projects will be showcased to computational experts who can reach out to the proposing parties and engage in a fruitful collaboration.
If you did not apply to the first Open Call, we invite you to do so at the beginning of 2024. Subscribe to our newsletter, we will inform you when the next call opens.
Additionally, if you are interested to put any open analysis problem you have on our Bartering Corner, please fill out this form.
If you need help quicker, we recommend Euro-BioImaging’s Web Portal, where you can access a network of experts in the field of image analysis. Please note that this service may involve associated costs, but access funds for certain research topics are available through initiatives such as ISIDORe.
by Caterina Fuster-Barceló
The AI4Life Hackathon on Web and Cloud Infrastructure for AI-Powered BioImage Analysis recently took place at SciLifeLab in Stockholm, Sweden. Organized by Wei Ouyang of KTH Sweden, in partnership with AI4Life and Global BioImaging, the event aimed to bring together experts in the field to discuss and design advanced web/cloud infrastructure for bioimage analysis using AI tools. Participants from both academia and industry worldwide attended, showcasing platforms like BioImage Model Zoo, Fiji, ITK, Apeer, Knime, ImJoy, Piximi, Icy, and deepImageJ. Read more in this article written by the project partners in FocalPlane.
