AI4Life

AI4Life_Logo
Menu

Category: News

Categories
News

AI4Life teams up with the Galaxy Training Network to enhance training resources

AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources

by Caterina Fuster-Barceló

In an exciting collaboration, AI4Life has joined forces with the Galaxy Training Network (GTN) project to revolutionize the way researchers access training materials. The GTN, known for its dedication to promoting FAIR (Findable, Accessible, Interoperable, and Reusable) and Open Science practices globally, now incorporates AI4Life to expand its training offerings.

Through this collaboration, BioImage Model Zoo (BMZ) and AI4Life trainers have developed videos and slides to introduce the community to the BMZ, demonstrate proper utilization, and guide contributions. This exciting development allows the BMZ to reach a wider audience within the research community and offers a simplified, visual approach to understanding and utilizing the BMZ.

This collaboration between the BMZ and GTN opens up new opportunities for researchers to access training materials and gain a better understanding of the BMZ’s capabilities. By making the process more accessible and intuitive, the BMZ aims to facilitate its adoption among researchers from diverse backgrounds.

The integration of the BMZ into the GTN project represents a significant advancement in training resources, empowering researchers worldwide and fostering collaboration within the scientific community. Stay tuned for upcoming training materials that will unlock the full potential of the BMZ for your research pursuits.

Categories
News

BioImage Model Zoo joins Image.sc forum as a Community Partner

BioImage Model Zoo joins Image.sc forum as a Community Partner

by Caterina Fuster-Barceló

The BioImage Model Zoo (BMZ) has been incorporated as a Community Partner of the Image.sc forum, a discussion forum for scientific image software sponsored by the Center for Open Bioimage Analysis (COBA). The BMZ is a repository of pre-trained deep learning models for biological image analysis, and its integration into the Image.sc forum will provide a platform for the community to discuss and share knowledge on a wide range of topics related to image analysis.

The Image.sc forum aims to foster independent learning while embracing the diversity of the scientific imaging community. It provides a space for users to access a wide breadth of experts on various software related to image analysis, encourages open science and reproducible research, and facilitates discussions about elements of the software. All content on the forum is organized in non-hierarchical topics using tags, such as the “bioimageio” tag, making it easy for people interested in specific areas to find relevant discussions.

As a Community Partner, the BMZ joins other popular software tools such as CellProfiler, Fiji, ZeroCostDL4Mic, StarDist, ImJoy, and Cellpose, among others. The partnership means that the BMZ will use the Image.sc forum as a primary recommended discussion channel, and will appear in the top navigation bar with its logo and link.

The Image.sc forum has been cited in scientific publications, and users may reference it using the following citation:

Rueden, C.T., Ackerman, J., Arena, E.T., Eglinger, J., Cimini, B.A., Goodman, A., Carpenter, A.E. and Eliceiri, K.W. “Scientific Community Image Forum: A discussion forum for scientific image software.” PLoS biology 17, no. 6 (2019): e3000340. doi:10.1371/journal.pbio.3000340

The integration of the BMZ into the Image.sc forum will undoubtedly facilitate knowledge-sharing and collaborative efforts in the field of biological image analysis, benefiting researchers, developers, and users alike.

Categories
News

Outcomes of the First AI4Life Open Call

Outcomes of the First AI4Life Open Call

 

by Beatriz Serrano-Solano & Florian Jug

We are thrilled to announce that we received an impressive number of 72 applications to the first AI4Life Open Call!

The first AI4Life Open Call was launched in mid-February and closed on March 31st, 2023. It is part of the first of a series of three that will be launched over the course of the AI4Life project.

AI4Life involves partners with different areas of expertise, and it covers a range of topics, including marine biology, plant phenotyping, compound screening, and structural biology. Since the goal of AI4Life is to bridge the gap between life science and computational methods, we are delighted to see so much interest from different scientific fields seeking support to tackle scientific analysis problems with Deep Learning methods.

We noticed that the most prominent scientific field that applied was cell biology, but we also received applications from neuroscience, developmental biology, plant ecology, agronomy, and marine biology, as well as aspects of the medical and biomedical fields, such as cardiovascular and oncology.

Why did scientists apply? What challenges are they facing?

Applications were classified based on the type of problem that will need to be addressed. We found that improving the applicant’s image analysis workflow was the most common request (67 applications out of 72). This was followed by improving image analysis data and/or data storage strategy, training data creation, and consultancy on available tools and solutions. Of course, we also welcomed more specific challenges that were not falling within any of those categories.

How have applicants addressed their analysis problem so far?

We were pleased to see that most of the applicants had already analyzed the data, but half of them were not fully satisfied with the outcomes. We interpreted this as an opportunity to improve existing workflows. The other half of the applicants were satisfied with their analysis results, but longed for better automation of their workflow, so it becomes less cumbersome and time-consuming. Around 20% of all applications haven’t yet started analyzing their data.

When asked about the tools applicants used to analyze their image data, Fiji and ImageJ were the most frequently used ones. Custom code in Python and Matlab is also popular. Other frequently used tools included Napari, Amira, Qupath, CellProfiler, ilastik, Imaris, Cellpose, and Zen.

What kind of data and what format do applicants deal with?

The most common kind of image data are 2D images, followed by 3D images, multi-channel images, and time series.

Regarding data formats, TIFF was the most popular one, followed by JPG, which is borderline alarming due to the lossy nature of this format. AVI was the third most common format users seem to be dealing with. CSV was, interestingly, the most common non-image data format. 

Additionally, we asked about the relevance of the metadata to address the proposed project. 17 applicants didn’t reply, and 24 others did, but do not think metadata is quite relevant to the problem at hand. While this is likely true for the problem at hand, these responses show that the reusability and FAIRness of acquired and analyzed image data is not yet part of the default mindset of applicants to our Open Call.

How much project-relevant data do the applicants bring?

We found that most of the data available was large, with most of them in the range between 100 and 1000 GB, followed by projects that come with less than 10 GB, between 10 and 100 GB, and less than 200 MB. 14 projects had more data than 1 TB to offer.

Is ground truth readily available?

We also asked about the availability of labelled data and provided some guidelines regarding the kind and quality of such labels. We distinguished: (i) Silver ground truth, i.e. results/labels good enough to be used for publication (but maybe fully or partly machine-generated, and (ii) gold ground truth, i.e. human curated labels of high fidelity and quality.

The majority of applicants (40) had no labelled data or only very few examples. The rest have silver level (8), a mix of silver and gold levels (10) and gold level (14) ground truth available. The de-facto quality of available label data is, at this point in time, not easy to be assessed, but our experience is that users who believe to have gold-level label data are not always right.

Is the available data openly shareable?

To train Deep Learning models, the computational experts will need access to the available image data. We found that only a small portion of applicants were not able to share their data at all. The rest is willing to share either all or at least some part of their data. When only parts of the data are sharable, reasons were often related to data privacy issues or concerns about sharing unpublished data.

What’s next? How will we proceed?

We are currently undergoing an eligibility check and the pool of reviewers will start looking at the projects in more detail. In particular, they will rank the projects based on the following criteria:

  • The proposed project is amenable to Deep Learning methods/approaches/tools.
  • Does the project have well-defined goals (and are those goals the correct ones)?
  • A complete solution to the proposed project will require additional classical routines to be developed.
  • The project, once completed, will be useful for a broader scientific user base.
  • The project will likely require the generation of significant amounts of training data.
  • This project likely boils down to finding and using the right (existing) tool.
  • Approaches/scripts/models developed to solve this project will likely be reusable for other, similar projects.
  • The project, once completed, will be interesting to computational researchers (e.g. within a public challenge).
  • The applicant(s) might have a problematic attitude about sharing their data.
  • Data looks as if the proposed project might be feasible (results good enough to make users happy).
  • Do you expect that we can (within reasonable effort) improve on the existing analysis pipeline?

The reviewers will also identify the parts of the project that can be improved, evaluate if deep learning can be of help and provide an estimation of the time needed to support the project.

We will keep you posted about all developments. Thanks for reading thus far! 🙂

Categories
News past events

AI4Life at Focus on Microscopy

AI4Life at Focus on Microscopy

14 April 2023

by Estibaliz Gómez de Mariscal

AI4Life was present this year at the Focus on Microscopy (FOM) 2023 conference in Porto, Portugal. 

FOM is a yearly conference series presenting the latest innovations in optical microscopy and its applications to life sciences. 

This year, the BioImage Model Zoo was presented again in one of the two dedicated oral sessions for image analysis under the title “BioImage Model Zoo: Accessible AI models for microscopy image analysis in one-click”. We highlight two of the most exciting discussion topics around AI4Life: “We need more deep-learning model benchmarks tailored for direct applications in life sciences” and “How can I upload my work to the BioImage Model Zoo”.

Remarkably, this year there was for the first time a dedicated section about smart microscopy where hybrid approaches using deep learning for adaptive optics and data-driven acquisitions were presented.

AcknowledgementS
Categories
News

Icy joins as a Community Partner!

Icy joins as a community partner!

by Carlos Garcia-López-de-Haro

The Bioimage Analysis software Icy has officially joined the BioImage Model Zoo as a Community Partner! This means that the Icy software will soon be compatible with the Deep Learning (DL) models present in the BioImage.io repository.

Icy is a powerful, open-source software designed for bioimage analysis, with features including visualization, annotation, graphical programming, and more. Now, with the compatibility with BioImage Model Zoo, Icy will further enhance its capabilities by leveraging the power of Deep Learning to analyze complex biological images better.

Meanwhile, Icy users will be encouraged to upload new models and datasets to the BioImage.io website, improving the collaboration and pushing the Bioimage Analysis field forward. The plugin to run Deep Learning models in Icy is in its final stage of development and it will be released soon. The Icy team is also providing the backend of their plugin as an independent Java library to run any Deep Learning model from various of the supported DL frameworks by the BioImage Model Zoo (Tensorflow 1, Tensorflow 2, Pytorch and Onnx) in an easy way.

Categories
News

New videos in the AI4Life YouTube channel

First videos in the AI4Life YouTube channel

 

The AI4Life YouTube channel is officially inaugurated! It features two training videos currently, with more to come in the future. The first two videos mark the beginning of a new playlist for training, showing how to upload models to the BioImage Model Zoo and the cross-compatibility of these models, which allows researchers to use them with different software tools and platforms.

We look forward to seeing more content on this channel in the future.

Categories
News

Summary of recommendations from the AI4Life & BioImage Archive FAIR AI workshop

Summary of recommendations from the AI4Life & BioImage Archive FAIR AI Workshop

27 February 2023

by Teresa Zulueta-Coarasa

The summary of recommendations gathered in the AI4Life & BioImage Archive FAIR AI Workshop that took place on January 24-25, 2023, is now public in Zenodo:

https://doi.org/10.5281/zenodo.7681687

A huge thank you to all the workshop participants for providing feedback on the summary. All the discussions, comments and suggestions have provided a great starting point for the manuscript with recommendations that will follow. We are looking forward to implementing the recommended updates on the BioImage Archive!

AcknowledgementS
Categories
News

Celebrating the International Day of Women and Girls in Science

Celebrating the International Day of Women and Girls in Science

Milan, 11 February 2023

At the recent Hackathon “Deep Learning in Fiji”, organized by AI4Life and Global Bioimaging at the Human Technopole, several talented and accomplished women engineers made their mark.

As the world celebrated the International Day of Women and Girls in Science during the Hackathon, we took the opportunity to highlight the contributions of the female participants to the hackathon. Read below their trajectories and accomplishments.

Caterina Fuster-Barceló

Caterina is a post-doctoral researcher immersed in AI4Life with Dr Arrate Muñoz Barrutia. In December 2022, she defended a PhD in Computer Science and Technology with a cum-laude mention obtained at Carlos III University of Madrid (UC3M), Spain, under the supervision of Dr Pedro Peris and Dr Carmen Cámara. She possesses a BSc in Telematics Engineering from the University of the Balearic Islands (UIB) and an MSc in Cybersecurity for the UC3M. One of her great passions is sharing the knowledge she obtained these last years with different communities. For this reason, she is participating in Skype A Scientist and has explained her thesis to people from different backgrounds at different conferences that you will find on her website.

Hackathons are a great opportunity to meet personally who you are working with. Being able to talk and work face to face is a rich experience that sometimes we forget how important it is. So glad that I have met those amazing people from all around the globe to share knowledge, skills and drinks!
Caterina Fuster-Barceló

Estibaliz is a mathematician by training and an expert in biomedical image analysis. She did her PhD at Universidad Carlos III de Madrid (Spain) with Prof. Arrate Muñoz-Barrutia and Prof. Denis Wirtz on the study of 3D cancer cell motility. Currently, she’s an EMBO postdoctoral fellow within the group of Prof. Ricardo Henriques at the Instituto Gulbenkian de Ciência in Portugal. She is heavily involved in the bioimage analysis community, more particularly in the development of deepImageJ, ZeroCostDL4Mic, DeepBacs and the BioImage Model Zoo. She also collaborates in the Cell Tracking Challenge and she’s a trainer in NEUBIAS training school for BioImage Analysts, EMBO practical courses on image processing, EMBL-EBI courses for Microscopy Data Analysis, Neurophotonics Summer School at CERVO institute & Universidad Laval, and the DL@MBL. Find out more about her career on her website and Twitter account.

In AI4Life, Estibaliz works on the connection between the resources that build models (ZeroCostDL4Mic) and consume them (deepImageJ), and the BioImage Model Zoo.

Estibaliz Gomez de Mariscal
Fiona Inglis

Fiona is currently a Research Software Engineer developing QuPath, a software for viewing, processing and quantifying microscopic images with a specific focus on large whole-slide images. She was first exposed to the imaging world while working as a slide-scanning imaging technician for the University of Edinburgh, after her first BSc in infectious diseases. 2 years on, she switched her career path to web development as she wanted to pursue programming full-time after enjoying scripting imageJ macros for researchers. Fiona completed another BSc alongside working, this time in Software Development, and then she found out about the opportunity to be part of Pete Bankhead’s growing QuPath team. This role has allowed her to combine her interests and has introduced her to a fantastic community, working together to put cutting-edge AI tools into researchers’ hands.

This was my first hackathon and therefore I planned to absorb as much knowledge as possible from those around me. Being at an in-person event after working remotely for so long was very refreshing and hugely beneficial to knowledge sharing and discussions. I spent the time understanding QuPaths current integration of deep learning, exploring the tools developed by others at the event and ways to increase the compatibility of these tools with each other.

Fiona Inglis

Lucia is a biomedical and computer science engineer, working on AI4Life with Dr Arrate Muñoz Barrutia. She did an MSc in Information Engineering for Health in order to use her skills to make deep learning accessible and easy to use in the Life Sciences field. She is now involved in the development of new tools and resources for biomedical image analysis and the improvement of some currently existing like DeepimageJ.

In the past years, she has been involved in projects related to neurodegeneration and rare blood diseases, trying to understand these conditions and find new ways to diagnose them. She is committed to inspiring new generations to pursue STEM careers and making science easy to understand for people not in the field. For that matter she has been part of some initiatives like #JuntosXElCancer, creating didactical biomedical engineering content related to cancer research; and she has also been part of CEEIBIS, the national committee for biomedical and health engineering, organizing talks and events for students that want to pursue a career in this field. Know more about Lucia on her website.

During these days, I have been Improving deepImageJ plugin, to make it easier to use and creating new ImageJ plugins for image processing, using both classical and deep learning tools.
Lucia Moya-Sans
Lucia Moya-Sans
Beatriz Serrano-Solano

Beatriz Serrano-Solano is a software engineer with a PhD in Computational Biology from the University of Málaga (Spain). After successfully defending her thesis, she embarked on a journey to Germany where she continued her academic pursuits as a postdoctoral researcher at EMBL Heidelberg. There, she participated in scientific projects for the European project EOSCpilot and later EOSC-Life. Later, and for a bit more than two years, she served as the community manager for the European Galaxy project, showcasing her expertise in project management and community building. Today, she holds the position of Scientific Project Manager at Euro-BioImaging, where she is involved in the European project AI4Life.

In AI4Life, Beatriz leads the work package for Communication, Outreach and Training, being also heavily involved in the organisation of the Open Calls and Challenges that will take place during the 3 years of the project.

Categories
News

Press Release

Press release

25.10.2022

Bridging Artificial Intelligence and Machine Learning with Bioimage Analysis

The AI4Life project launched today, marks an exciting chapter in the computational and life science research communities. The €4 million Horizon Europe funded project aims to create accessible, harmonized, and interoperable AI tools and methods for solving today’s microscopy image analysis problems.

THE GAP

Machine learning (ML) has accelerated frontier research in the life sciences, but democratized access to such methods is not a given. Limited access to necessary hardware/software and expertise combined with insufficiently documented methods hinder life science researchers from harnessing the power of such tools. Furthermore, while modern Artificial Intelligence (AI)-based methods typically generalize well to unseen data, no standard exists for sharing and fine-tuning pretrained models between different analysis tools. Compounding the issue, existing user-facing platforms operate entirely independently, often failing to comply with FAIR data and Open Science standards. Furthermore, the staggering pace of AI and ML development make it impossible for the non-specialist to stay up to date. Hence, urgent services and infrastructures to solve such problems are required to expand cutting edge life science research.

THE BRIDGE

The 10-partner consortium will build an open, accessible, community-driven repository of FAIR pre-trained AI models and develop services to deliver these models to life scientists, including those without substantial computational expertise. AI4Life will provide direct support and ample training activities to prepare life scientists for responsible use of AI methods. Additionally, AI4Life will drive community contributions of new models and interoperability between analysis tools. AI4Life will also facilitate Open calls and public Challenges aimed at providing state-of-the-art solutions to unsolved image analysis problems in life science research. 

AI4Life brings together AI/ML researchers, developers of open-source image analysis tools, providers of European-scale storage and compute services, and European life science Research Infrastructures – all united behind the common goal to enable life scientists to benefit from the untapped, tremendous power of AI-based analysis methods.

THE CORE OBJECTIVES

  • Democratize availability of AI-based image analysis methods
  • Establish standards for the submission, storage, and FAIR access of reference data, reference annotations (ground-truth), trained AI models, and trainable AI methods
  • Simple model deployment, sharing and dissemination through a new developer-facing service
  • Organize Open calls and Challenges for image analysis problems
  • Empower common image analysis platforms with AI integration
  • Organizing outreach and training events

THE TEAM

Our multidisciplinary team of experts in computational and life sciences as well as 4 European Research Infrastructures.

10

PARTNERS

3

YEARS

8

COUNTRIES

4M

EUROS