by Beatriz Serrano-Solano
We are excited to share two interviews featuring Joran Deschamps, image analysis researcher and research software engineer coordinator at Human Technopole; and Craig Russell, AI engineer at EMBL-EBI, who discuss their views on AI4Life and its impact on the field of bioimaging and AI research.
Discover more about AI4Life through the eyes of these experts.
The deadline for the AI4Life Challenge has been extended to the 31st of July. There’s still time to participate!
by Beatriz Serrano-Solano
We’re excited to announce an interview with Joanna Hård, CEO of Amun AI, where she discusses the scope of this innovative spin-off from AI4Life and KTH.
Amun AI’s flagship product is a plug-and-play version of the Hypha platform, which includes confidential computing. It is designed for large-scale data management and AI model serving.
Discover the synergy between Amun AI and AI4Life, and learn how their collaboration is advancing bioimaging and AI research.
Watch the full interview:
by Beatriz Serrano-Solano
We are pleased to announce a new section on the AI4Life website dedicated to our governance structure. If you want to know who is behind each project body, visit the “Governance” page under the “About Us” section.
On this page, you will find detailed information about:
– Project Management Team: The core team responsible for the day-to-day operations of AI4Life.
– Steering Committee: The advisory group guiding the project’s strategic direction.
– General Assembly: The broader assembly of stakeholders involved in the project.
Additionally, we have two bodies that include external members:
– Executive Board: A team of senior experts providing high-level oversight.
– Open Calls Selection Committee: The committee responsible for evaluating submissions and selecting projects for support.
This new governance page aims to provide transparency and insight into the dedicated individuals and committees driving AI4Life forward.
🔗 Visit the Governance Page at https://ai4life.eurobioimaging.eu/governance
by Wei Ouyang, Estibaliz Gómez de Mariscal, Craig Russell, Vera Galinova, Mehdi Seifi & Beatriz Serrano-Solano
The AI4Life May Hackathon held in Milan ended with successful contributions and progress made across several key areas. Here’s a summary of the event’s highlights:
Consultation Phase of the Second Open Call
The consultation phase of the second Open Call started during the week of the hackathon. In-person participants engaged in calls with the Open Call applicants to advise them on their projects and better assess the complexity of possible solutions. These consultations addressed a variety of bioimage analysis and deep learning challenges, providing valuable insights and advice to projects from all over Europe.
Public Challenges
Participants provided critical feedback on the AI4Life-MDC24 challenge pages hosted on the Grand Challenge platform. This included testing data downloads and attempting to train their own denoising models.
Infrastructure Improvement for Better Scalability
A significant milestone was achieved in upgrading Hypha and the BioEngine. This involved:
Enhancements also included integrating a Ray cluster to manage compute tasks, providing a scalable and efficient alternative to the Kubernetes API. This upgrade is essential for the fine-tuning of models, ensuring better performance and resource management.
Model Uploader
Efforts were concentrated on developing the new model uploader and the model review feature. Detailed coordination plans were discussed for migrating the old collection to the new system, ensuring a smooth transition and improved functionality.
Crowdsourcing Interactive Annotations
Participants worked on the creation of a crowdsourcing annotation tool, enabling collaborative image annotations by multiple users. This tool is accessible here.
Interactive Annotation Tool
Another project focused on an interactive annotation tool based on Kaibu and micro-SAM, resulting in a browser-compatible prototype.
BioImage.IO-Google Colab Project
A project was prototyped to connect the BioEngine with Kaibu for collaborative annotations. This included documenting the bioimageio_core and bioimageio_spec and updating an example notebook for local or Google Colab use, showcasing annotation with BioImageIO-Colab and fine-tuning a Cellpose model.
Thank you to all the participants for making the best out of this event and providing the scientific community with new improvements and achievements.
by Vera Galinova and Beatriz Serrano-Solano
We are happy to announce the launch of the first AI4Life challenge aimed at improving denoising techniques for microscopy images.
Noise introduced during the image acquisition process can degrade their quality and complicate interpretation. But deep learning can help with that!
The challenge focuses on unsupervised denoising to be applied to four datasets featuring two types of noise: structured and unstructured.
To participate, please visit the dedicated website and the Grand Challenge page on which the challenge is hosted:
by Beatriz Serrano-Solano
AI4Life launched its second Open Call on January 22nd, and applications were accepted until March 8th. We’re very happy to share that we received a total of 51 applications for the second AI4Life Open Call!
The applications span a wide range of fields, including developmental and marine biology, as well as cancer research. But that’s not all—we also received submissions from areas such as plant biology, parasitology, ecology, biophysics, microbiology, immunology, and many more.
Similar to the first open call, most applicants focused on improving their image analysis workflows. However, unlike the first Open Call, we did not offer consultancy as an option to choose, as it is now an implicit step in the process.
Before finalizing project selections, we will hold consultancy calls with a number of project applicants to offer quick guidance that could help researchers find solutions. Projects requiring deep learning support will then be prioritized for the final selection.
Nearly three-quarters of the applicants had already analysed the data, a higher percentage compared to last year. However, only one-fifth of them had not done so yet. The remaining applicants had already analyzed the data and found the outcome satisfactory. Last year, twice the fraction of projects were satisfied with the outcome compared to this time.
When asked about the tools used to analyze their image data, Fiji and ImageJ remain the most popular choices, followed by custom scripts and commercial software.
In terms of data format, we observe a change in the trend compared to the first call. Now, 3D images are slightly more frequent than 2D images, reversing the trend we observed last time.
TIFF remains the most popular format, while the second group comprises more proprietary file formats. We are thrilled to observe that the Next Generation File Format (NGFF) has appeared this time!
The majority of projects (45 out of 51) manage data in the order of gigabytes or above. Only 6 projects involve data sizes up to a few hundred megabytes. This highlights the prevalence of larger-scale datasets within the applicants, suggesting a growing demand for proper data management and processing capabilities.
When asked about the availability and percentage of labelled data, approximately half of the applicants (23 out of 51) reported a lack of sufficient labelled data. Despite a variance in the definition of labelled data compared to the first open call, we observe a comparable trend. Additionally, the proportion of projects indicating high-quality labels has decreased compared to the previous call.
In the application form for this second open call, we introduced an additional question regarding the percentage of labelled data. Over three-quarters of the projects have less than 25% of their data labelled, while approximately 20% of the projects have more than half of their data labelled.
Interestingly, the ratio of projects that can provide access to all their data remains consistent compared to the first open call. This time, we’ve introduced the option of exclusively sharing the controls. The reason behind this decision is that projects unable to share any data are not eligible for support in applying deep learning to their research project.
We have completed the eligibility check and expert reviews for the submitted projects. Projects were reviewed by 17 experts, resulting in an average of 3 reviews per project. The reviews have been aggregated, and scores have been computed to rank the projects based on these reviews. As a result, a preselection of projects has been made, and these applicants will soon be notified.
What to expect from the consultation phase?
During the consultation call, selected applicants will have the opportunity to engage with experts who will provide insights, tips, existing tools and recommendations to guide their project. Following the consultation phase, a subset of projects will be chosen to receive expert support based on their potential and need for deep learning support.
by Beatriz Serrano-Solano and Joran Deschamps
AI4Life recently organised a hackathon aimed at enhancing the capabilities of the BioImage Model Zoo. Participants from across Europe gathered for a week-long event hosted at EMBL Heidelberg in Germany.
The event kicked off with a round of introductions, allowing participants to outline their personal goals for the week and to form teams that would tackle various project ideas.
Model uploader
One of the key focus areas was to put the final touches to a new model uploader, aimed at simplifying the process of uploading models to the BioImage Model Zoo. The uploader will no longer rely on external platforms like Zenodo; instead, models will be hosted internally and authentication will be requested for contributors who want to upload a model. One of the teams worked to simplify authentication procedures and optimize model uploads to S3 by integrating Google authentication, providing a unified system that would enhance the overall user experience.
Infrastructure improvement
Teams dedicated their efforts to refining Continuous Integration (CI) processes, which have now been migrated to the collection-bioimage-io GitHub repository. The uploader now triggers the CI workflow, automating the process of pushing models to the designated storage location on S3.
JupyterHub and DL4MicEverywhere
Another focus area involved transitioning the infrastructure for JupyterHub from Google Colab to Google Cloud, providing users with a more robust and flexible environment.
Model quantization
Model quantization allows making networks smaller and faster without loss of precision. We held discussions describing current state of the art.. As an example of the performance gains, a 3D Unet model from the BioImage Model Zoo reduced the inference of a batch of images from 60 ms to 30 ms.
Hypha launcher
Hypha can now launch BioEngine (triton-server) on a Slurm cluster using Apptainer. Additionally, a service-id option is now implemented in the BioEngine web client to easily switch the execution backend to high-performance computing (HPC) environments. Furthermore, BioEngine can now be launched on desktop environments.
Model export to new specifications
This team focused on exporting models using the new specifications. Additionally, the team explored approaches to export CellPose models.
Documentation enhancement
This project was split into two phases: firstly, restructuring the current documentation and secondly, creating new needed documentation for the BioImage Model Zoo. Community input and feedback are highly encouraged in this project!
Second Open Call
The deadline for the second Open Call was March 8th. During the hackathon, we had the opportunity to engage with all the reviewers, many of whom were participating in the event. Projects were assigned to each reviewer, kickstarting officially the review process.
Thank you to everyone who contributed either onsite or online. It was a pleasure to work with this engaged group of people. And thank you to the AI4Health innovation cluster for supporting this event. We look forward to meeting you at the next event!
by Beatriz Serrano-Solano and Dorothea Dörr
Have you ever wondered why the AI4Life logo is a charming giraffe? Well, you’re not alone! AI4Life decided to host a contest to unravel the mystery, and the responses were so good that we had to pick three winners! Let’s dive into the proposed theories:
Because you’ve got to stand out somehow (both literally and figuratively)
Because we do things so cool, everyone who is not part of us gets a looooong neck… 😉
With its long neck, it keeps an overview of all other animals in the zoo.
AI-generated based on project objectives. ChatGPT or similar.
From all the answers received, 3 winners were selected:
Being the tallest animal on the earth, the giraffe has the perfect general perspective of what's going around. Thanks to its long neck, it can both, aim to reach the highest appealing branch and get down into the lower details of the animal world. Not only it's a friendly non-predator animal that inspires peacefulness, but rarely other predators attack it, thus becoming a unique symbol of long-standing equilibrium, collaboration and unity for our beloved BioImage Model Zoo.
To understand why you chose a giraffe as the logo for AI4Life is to consider what makes giraffes special. You might immediately think of their long necks, but the unique aspect that likely inspired this choice is that giraffes communicate through infrasounds, as they are relatively silent animals. This ability allows them to communicate over significant distances. So, when pondering the connection between AI4Life and a giraffe, the commonality lies in the fact that even though partners may be physically distant, they can effectively communicate, akin to the way giraffes do through... infrasounds.
It's a reference to the Lamarck theory of evolution: the great effort made on the neck of generations of giraffes (re-iterations of machine learning algorithms) has determined that only the most successful organism (the best algorithm) would find a solution to the problem. Giraffe legs: it's a power plug of a computer.
What a journey of creativity and imagination! From the giraffe’s long neck symbolizing reaching new heights to its unique spots representing valuable data points, the interpretations have been as diverse as they are exciting. Big shoutout to all who joined the fun! And a huge round of applause to our awesome winners!
by Dorothea Dörr and Beatriz Serrano-Solano
AI4Life has been selected by the European Commission Directorate-General for Research and Innovation (RTD) to be showcased at the European Research and Innovation (R&I) Days 2024. The event took place during the Research and Innovation Week on the 20th and 21st of March 2024 in Brussels.
The R&I Days 2024 are an exceptional opportunity for AI4Life to be in the spotlight of policymakers, researchers, stakeholders, and the general public who gathered to discuss and influence the future of research and innovation in Europe and beyond.
For more information, visit the R&I Days 2024 website.
Ready to dive deep into innovation? The European Research & Innovation Days 2024 are in full swing!
— European Commission (@EU_Commission) March 20, 2024
Join us on 20 and 21 March to explore cutting-edge EU-funded R&I projects and have your say on how we can make Europe more competitive, greener and fairer.
🔗 More info ↓
