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AI4Life launches digital badges

AI4Life launches digital badges

by Beatriz Serrano-Solano

AI4Life is now using Badgr/Canvas Badges to issue and share digital badges with contributors to the project. Here you’ll find how to use them.

Step 1. Create an account in Badgr

If you’ve received an email from Badgr (noreply@badgr.com) with the subject “You earned <name of the badge> badge!”, congratulations, you can now add it to your social media channels!

To access your new badge, you need to create an account in Badgr if you don’t have one already:

https://badgr.com/

The badge will appear in your Badgr backpack (Figure 1).

Figure 1. Backpack view at Badgr
Step 2. Share your badge on LinkedIn

Now you’re ready to share the badge in your networks using the provided personalised URL, here’s how!

Click on “share” on the badge that appears on your backpack and then on “Add to profile” (Figure 2).

Figure 2. Pop-up window to share the Badge in social media

A new pop-up window will appear with the name of the badge (e.g. “Reviewer of the 1st/2nd AI4Life Open Call” or “Awardee project of the AI4Life Open Call“) and the issuing organization (Figure 3). At this stage, you need to change the issuing organization to “AI4Life EU project” (should autocomplete when you start typing) as you can see in Figure 4.

Figure 3. Pop-up window with the original issuing organization
Figure 4. Updated issuing organization

Click on save and you’re good to go!

Step 3. Check your profile and share with your contacts

Now you can share on other platforms too!

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Opportunity alert: HT launches a call for access to national facilities for data handling and analysis

Opportunity alert: HT launches a call for access to national facilities for data handling and analysis

by Joran Deschamps & Damian Edward Dalle Nogare

As part of the National Facility for Data Handling and Analysis at Human Technopole, the Bioimage Analysis infrastructure unit aims at providing state-of-the-art analysis of biomedical imaging data to researchers working at publicly-funded Italian institutions. As part of this mission, we are pleased to announce our 2024 open call for bioimage analysis projects. This open call allows users to submit analysis projects to our facility based on data generated at their own institutes. Successful applicants will collaborate with our analysis team to define the scope and goals of the project, and to develop analysis pipelines to meet their scientific objectives.

This call is currently open, with the first application review period scheduled for September. More information, including a description of the program, eligibility criteria, deadlines, and instructions to apply can be found at this link (pdf).

 

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Second AI4Life Open Call: Announcement of selected projects

Second AI4Life Open Call: 
Announcement of selected projects

by Joran Deschamps, Florian Jug & Beatriz Serrano-Solano

The second AI4Life open call received 51 applications from diverse scientific disciplines (see this online report for more details).

In contrast to our first open call, the selection procedure has slightly evolved. We are now distinguishing two phases, a consultancy phase, and a project execution phase. The motivation for this evolution stemmed from the observation that we can maximize the value of our service to the scientific community by providing feedback (e.g. feasibility, existing tools, approaches we would take) to a larger number of projects, and then taking on a small number of projects for more in-depth support (similar to last year’s first open call) based on informed opinions of experts regarding the readiness of the project.

The selection of projects, for both phases, was conducted by an international group of project reviewers, similar to the procedure of last year.

Projects selected for Phase 1: Project Consultation

  1. Automated image analysis for mouse embryos phenotyping (#1)
  2. Analysis of necrotic cores in diabetes (#3)
  3. Bring isotropy to Volume EM (#7)
  4. Automated species annotation in turbid waters (#11)
  5. Segmenting dynamic 3D cell shape in crowded epithelia (#12)
  6. Optimizing calcium image acquisition with machine learning denoising algorithms (#13)
  7. Diatom symbioses at planetary scale (#14)
  8. Automated segmentation of actin filaments in intact cells (#21)
  9. Automated and Integrated Tunneling Nanotube (TNT) Detection and Counting (#22)
  10. Enhancing the effective resolution of intravital microscopy with digital expansion microscopy (#23)
  11. Automated myelin segmentation (#24)
  12. Ultrastructural protein mapping through Correlation of Light and Electron Microscopy (#26)
  13. The speed of life in trees – linking wood anatomy with lifespan and respiration (#28)
  14. Systematic quantitative characterization of mechanisms controlling spindle and nuclear scaling in tissue (#29)
  15. CKit- Mast Cell Tumor (#30)
  16. Novel regulators of chromatin response to DNA damage (#32)
  17. Scaling of actin networks with cell size (#34)
  18. NeuroScan – A 3D human motor neuron disease platform for high throughput drug screening  (#38)
  19. Cell Fate of Pax7-tdtomato transplanted cells in immune dysregulation during aging (#46)
  20. Correlating bacteria images from STORM and AFM microscopies in a combined analysis using registration methods (#51)

As the titles suggest, consulted projects covered a wide range of disciplines and topics. Consultation sessions were typically conducted with between two to four AI4Life experts and consisted of a one-hour discussion during which the experts could ask additional questions regarding the project and then provide opinions regarding the feasibility of the projects. Along with this kind of “reality check”, the experts also aimed at giving tangible advice on existing tools and methods that might help the consulted researchers in improving their workflows and achieving the desired analysis results.

Projects selected for Phase 2: Deep Learning project support

With the additional information from the consultancy phase at hand, the group of AI4Life experts came together and discussed the most promising projects to be taken on in phase 2 (deep-learning support). This decision was guided by a combination of project feasibility, readiness, re-usability by the community, and how well each project fits within the experiences and capabilities of the AI4Life team itself. These considerations were combined with the intention to maximize the utility and impact on the bioimage analysis community of this Open Call.

  1. Automated species annotation in turbid waters (#11*)
  2. Optimizing calcium image acquisition with machine learning denoising algorithms (#13)
  3. Diatom symbioses at planetary scale (#14*)
  4. Automated segmentation of actin filaments in intact cells (#21)
  5. Enhancing the effective resolution of intravital microscopy with digital expansion microscopy (#23)
  6. Ultrastructural protein mapping through Correlation of Light and Electron Microscopy (#26)
  7. NeuroScan – A 3D human motor neuron disease platform for high throughput drug screening (#38)
(*) Project goals were readjusted after consultancy between applicants and the AI4Life team.

Details about the reviewing procedure

Reviewing procedure

After assembling a panel of 17 international reviewers, we distributed anonymized projects among them. All personal and institutional information was removed, only leaving project-relevant data to be reviewed (including a short presentation and data examples). We aimed at receiving 3 independent reviews per project, requiring each reviewer to judge 9 projects in total.

Here is the list of questions we asked our reviewers via an electronic form:

  1. Please rank the following statements from 1 (Likely not) to 5 (Likely):
    1. The project has well-defined goals (and these goals are the correct ones).
    2. The proposed project will benefit from Deep Learning methods/approaches/tools.
    3. A complete solution will be purely deep learning (no intensive classical routine necessary).
    4. The project requires developing new approaches or tools.
    5. Approaches/scripts/models developed to solve this project will likely be reusable for other, similar projects.
    6. The project, once completed, will be useful for a broader scientific user base.
    7. The project’s current analysis pipeline can easily be improved.
    8. The project cannot be solved by simply offering one or two consultations to the users.
    9. The project seems feasible in a few months (of part time work).
    10. The project, once completed, will be interesting to computational researchers (e.g. within a public challenge).
    11. The applicants will have no problem sharing their data publicly.
  2. What are the key sub-tasks the project needs us to improve?
  3. How long would you expect it to take (in person/days) to generate sufficient training data (if applicable)?
  4. Once there is enough training data available, what workload do you anticipate in developing a viable solution for the proposed project and why?
  5. What is your estimated minimum number of days for successfully working on this project?
  6. What is your estimated maximum number of days for successfully working on this project?
  7. Do suitable tools for solving this task exist? What would you use?
  8. On a scale from 1 to 10, how enthusiastic are you about this project?
  9. Would this project qualify as an AI4Life project?
  10. Would this project qualify for Consultation?
 Scoring projects according to reviewer verdicts

We first aggregated all reviews per project by averaging numerical values and concatenating textual evaluations. We then computed two different aggregated scores: consultation and support based on the reviewer evaluations:

Consultation score
  • [0.8] Solvable in 1-2 consultation sessions (= 5 – “Not solvable”)1
  • [0.2] Solution likely reusable
Support score
  • [0.2] Interesting for the computational community
    • [0.1] Enthusiasm2
    • [0.1] Interesting for challenge
  • [0.8] Quality & Feasibility
    • [0.2] Quality of the application
      • [0.1] Well-defined project
      • [0.1] Pipeline can be easily improved
      • Persons/day to generate training data (tiebreaker
    • [0.2] DL suitable
      • [0.15] DL applies
      • [0.05] only Deep Learning and not classical image analysis
    • [0.2] Impact
      • [0.1] Impactful in the broader community
      • [0.1] Solution likely reusable
      • Workload to find a solution with training data (tiebreaker
    • [0.2] Time effort
      • [0.1] Feasible in a few months
      • [0.05] Min days3
      • [0.05] Max days
1 “Solution likely reusable” calculated from “Not solvable in 1-2 consultations” (scale inversion)
2 Enthusiasm rescaled from 0-10 to 0-5
3 Min and Max days rescaled to linearly decrease from an optimum of 60 days (score = 5)
 Consultation phase
  1. After ranking the projects, we decided on a scoring threshold for both consultation and project scores so that we could provide about 20 hours of consultations. We invited all applicants whose project scored either >3 on the consultation score (11 projects) or >3.8 on the project score (12 projects), to a 1h consultation with a panel of typically two to four suitable experts. Two projects declined the invitation, and one project scored high on both scores, resulting in 20 consultations.
  2. After each consultation, the experts drafted a summary of their suggestions to the scientists, and their opinion regarding the feasibility and readiness of the project, in particular with respect to the AI4Life Open Call time frame.
  3. A second consultation was offered to a subset of the projects (7). While this was not initially planned, we decided that those projects would benefit from additional feedback and guidance within the time frame of a single additional consultancy session.

Final decisions were made during a general assembly of reviewers and AI4Life Experts who would later take on the selected projects. The decisions suggested during this meeting were brought to the Open Call Selection Committee, which checked the soundness of all decisions and unanimously accepted the phase 2 project selections.

Who was involved in the process?

And now? What’s next?

The applicants whose projects were selected are now being contacted by the AI4Life team. All other projects that expressed interest in being part of the AI4Life Bartering Corner will be contacted to prepare publication on the AI4Life website, so computational experts can reach out to the scientists behind any of these projects and start a collaboration. 

If you did not have the chance to apply to the second Open Call, there will be a third and last call at the end of 2024. Subscribe to our newsletter, and you will be informed about the opening of the next call!

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 some initiatives. We and Euro-Bioimaging will be happy to help you find suitable opportunities!

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Insights from the AI4Life team: Joran Deschamps and Craig Russell on AI4Life

Insights from the AI4Life team: Joran Deschamps and Craig Russell on AI4Life

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.

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Interview with Joanna Hård, CEO of Amun AI

Interview with Joanna Hård, CEO of Amun AI

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:

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New Governance page on the AI4Life website

New Governance page on the AI4Life website

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

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Open Call & Challenges Mini-Hackathon in Milan

Open Call & Challenges Mini-Hackathon in Milan

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:

  • Transitioning the deployment base image from Hypha to the BioEngine.
  • Building a separate Hypha image with necessary plugins to better support the BioEngine.
  • Setting up environment requirements to facilitate this transition, ensuring the new BioEngine image can include additional apps like Cellpose and ImageJ.

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.



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First AI4Life challenge launched: Denoising microscopy images

First AI4Life challenge launched: Denoising microscopy images

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:

https://ai4life-mdc24.grand-challenge.org/ai4life-mdc24/