Outcomes
Starting its journey in September 2022 and scheduled to conclude in August 2025, the AI4Life project is dedicated to bringing together the life science and computational communities. As we navigate through this timeframe, we are compiling a comprehensive overview of the significant outcomes we’ve achieved so far.
M1
D1, D2, D3, D4, D7, D9
D13, M2
D17, D19, M3, M4
D5
M5
M6
D14, D18, M7
D6, M8
D7
D8, D10, D15, D25, M9, M10, M11
M12
D11, D20, D24, M13
M14
D12, D16, D21, D22, M15, M16
| Item | Due Date | Description | WP |
|---|---|---|---|
| Deliverable | 30/11/2023 | Policy Brief | WP1 |
| Deliverable 1 | 28/02/2023 | Report on establishment of project management procedures and executive group | WP1 |
| Deliverable 2 | 28/02/2023 | Strategy for dissemination of information within the consortium | WP1 |
| Deliverable 3 | 28/02/2023 | Risk Management Plan | WP1 |
| Deliverable 4 | 28/02/2023 | Data Management Plan | WP1 |
| Deliverable 5 | 30/09/2023 | Cloud infrastructure (accessible from the AI4Life website) | WP2 |
| Deliverable 6 | 30/04/2024 | Deployment kit for user institutions to replicate the cloud infrastructure (accessible from the AI4Life website) | WP2 |
| Deliverable 7 | 28/02/2023 | Plan for dissemination and exploitation including communication activities | WP7 |
| Deliverable 7 | 30/06/2024 | Terms of service for the BMZ website and infrastructure | WP2 |
| Deliverable 8 | 31/08/2024 | Documentation on BioEngine application development (accessible from the AI4Life website) | WP2 |
| Deliverable 9 | 28/02/2023 | Terms of Service | WP3 |
| Deliverable 10 | 31/08/2024 | User-friendly versions of zero-code tools | WP3 |
| Deliverable 11 | 28/02/2025 | Documentation and Training Material Report | WP3 |
| Deliverable 12 | 31/08/2025 | Assessment report of the end-user experience | WP3 |
| Deliverable 13 | 31/05/2023 | Notebooks and algorithms repository (accessible from the AI4Life website) | WP4 |
| Deliverable 14 | 29/02/2024 | Deployment of a container system (accessible from the AI4Life website) | WP4 |
| Deliverable 15 | 31/08/2024 | Release of human-in-the-loop interfaces (accessible from the AI4Life website) | WP4 |
| Deliverable 16 | 31/08/2025 | Release resource and data sharing standards (accessible from the AI4Life website) | WP4 |
| Deliverable 17 | 31/08/2023 | Annotation standards and software, libraries and reference examples | WP5 |
| Deliverable 18 | 29/02/2024 | BIA infrastructure and deposition pipeline for versioned reference annotations | WP5 |
| Deliverable 19 | 31/08/2023 | First stable BioImage Zoo Model format + software tools, libraries and reference examples | WP5 |
| Deliverable 20 | 28/02/2025 | Model evaluation platform | WP5 |
| Deliverable 21 | 31/08/2025 | Report on all Open Call activities (annually updated and published on the AI4Life website, also available as PDF) | WP6 |
| Deliverable 22 | 31/08/2025 | Report on all Public Challenge activities (annually updated and published on the AI4Life website, also available as PDF) | WP6 |
| Deliverable 24 | 28/02/2025 | Report on AI4Life workshops, hackathons and communication activities with other LS RIs | WP7 |
| Deliverable 25 | 31/08/2024 | Report on FAIR guidelines followed in the consortium. | WP7 |
| Milestone 1 | 30/09/2022 | First meeting of the Project Management Executive Group | WP1 |
| Milestone 2 | 31/05/2023 | Tutorial material available | WP3 |
| Milestone 3 | 31/08/2023 | Public release of annotation format and supporting software | WP5 |
| Milestone 4 | 31/08/2023 | First AI4Life workshop held | WP7 |
| Milestone 5 | 31/10/2023 | The computing infrastructure provided for testing among community partners and connected to the website and released to the public | WP2 |
| Milestone 6 | 31/12/2023 | Year 1 Public Challenge for selected open call analysis problems is launched; benchmark implementation available | WP6 |
| Milestone 7 | 29/02/2024 | BioImage Model Zoo (BMZ) specification and examples released; all models in BMZ to spec | WP5 |
| Milestone 8 | 30/04/2024 | The deployment kit released for power users to set up their own computing infrastructure | WP2 |
| Milestone 9 | 31/08/2024 | User-friendly zero-code tools available | WP3 |
| Milestone 10 | 31/08/2024 | Release of extensible developer resources promoting contributions to the AI4Life tools repository | WP4 |
| Milestone 11 | 31/08/2024 | BMZ standards available on FAIRSharing.org | WP7 |
| Milestone 12 | 31/12/2024 | Year 2 Public Challenge for selected open call analysis problems is launched; benchmark implementation available | WP6 |
| Milestone 13 | 28/02/2025 | Model benchmarking service active | WP5 |
| Milestone 14 | 30/06/2025 | Final launch of improved BioImage Model Zoo website | WP2 |
| Milestone 15 | 31/08/2025 | Year 3 Public Challenge for selected open call analysis problems is launched; benchmark implementation available | WP6 |
| Milestone 16 | 31/08/2025 | Release of contribution guidelines, ensuring contributions meet standards, quality, and ethical review | WP4 |
Publications
5145082
1
apa
50
date
3268
https://ai4life.eurobioimaging.eu/wp-content/plugins/zotpress/
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Seifi, M., Dalle Nogare, D., Battagliotti, J. M., Galinova, V., Rao, A. K., Jouneau, P.-H., Archit, A., AI4Life Horizon Europe Programme Consortium, Beuttenmueller, F., Dörr, D., Ferreira, M. G., Fuster-Barceló, C., Galinova, V., García-López-de-Haro, C., Gómez-de-Mariscal, E., Hartley, M., Henriques, R., Hidalgo-Cenalmor, I., Jug, F., … Deschamps, J. (2025). FeatureForest: the power of foundation models, the usability of random forests. Npj Imaging, 3(1), 32. https://doi.org/10.1038/s44303-025-00089-9
Sorensen, L., Humenick, A., Poon, S. S. B., Han, M. N., Mahdavian, N. S., Rowe, M. C., Hamnett, R., Gómez-de-Mariscal, E., Neckel, P. H., Saito, A., Mutunduwe, K., Glennan, C., Haase, R., McQuade, R. M., Foong, J. P. P., Brookes, S. J. H., Kaltschmidt, J. A., Muñoz-Barrutia, A., King, S. K., … Rajasekhar, P. (2024). Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. Journal of Cell Science, jcs.261950. https://doi.org/10.1242/jcs.261950
Lei, W., Fuster-Barceló, C., Reder, G., Muñoz-Barrutia, A., & Ouyang, W. (2024). BioImage.IO Chatbot: a community-driven AI assistant for integrative computational bioimaging. Nature Methods, 21(8), 1368–1370. https://doi.org/10.1038/s41592-024-02370-y
Hidalgo-Cenalmor, I., Pylvänäinen, J. W., G. Ferreira, M., Russell, C. T., Saguy, A., Arganda-Carreras, I., Shechtman, Y., AI4Life Horizon Europe Program Consortium, Muñoz-Barrutia, A., Serrano-Solano, B., Barcelo, C. F., Pape, C., Lundberg, E., Jug, F., Deschamps, J., Ferreira, M. G., Hartley, M., Seifi, M., Zulueta-Coarasa, T., … Gómez-de-Mariscal, E. (2024). DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nature Methods. https://doi.org/10.1038/s41592-024-02295-6
Saguy, A., Nahimov, T., Lehrman, M., Gómez-de-Mariscal, E., Hidalgo-Cenalmor, I., Alalouf, O., Henriques, R., & Shechtman, Y. (2024). This microtubule does not exist: Super-resolution microscopy image generation by a diffusion model. bioRxiv. https://doi.org/10.1101/2023.07.06.548004
Franco-Barranco, D., Andrés-San Román, J. A., Hidalgo-Cenalmor, I., Backová, L., González-Marfil, A., Caporal, C., Chessel, A., Gómez-Gálvez, P., Escudero, L. M., Wei, D., Muñoz-Barrutia, A., & Arganda-Carreras, I. (2024). BiaPy: A unified framework for versatile bioimage analysis with deep learning [Preprint]. bioRxiv. https://doi.org/10.1101/2024.02.03.576026
Gómez-de-Mariscal, E., Del Rosario, M., Pylvänäinen, J. W., Jacquemet, G., & Henriques, R. (2024). Harnessing artificial intelligence to reduce phototoxicity in live imaging. Journal of Cell Science, 137(3), jcs261545. https://doi.org/10.1242/jcs.261545
García López De Haro, C., Dallongeville, S., Musset, T., Gómez-de-Mariscal, E., Sage, D., Ouyang, W., Muñoz-Barrutia, A., Tinevez, J.-Y., & Olivo-Marin, J.-C. (2024). JDLL: a library to run deep learning models on Java bioimage informatics platforms. Nature Methods, 21(1), 7–8. https://doi.org/10.1038/s41592-023-02129-x
Fuster-Barceló, C., García López De Haro, C., Gómez-de-Mariscal, E., Ouyang, W., Olivo-Marin, J.-C., Sage, D., & Muñoz-Barrutia, A. (2024). Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ [Preprint]. bioRxiv. https://doi.org/10.1101/2024.01.12.575015
Morgado, L., Gómez-de-Mariscal, E., Heil, H. S., & Henriques, R. (2024). The rise of data-driven microscopy powered by machine learning. Journal of Microscopy, 295(2), 85–92. https://doi.org/10.1111/jmi.13282
Pylvänäinen, J. W., Gómez-de-Mariscal, E., Henriques, R., & Jacquemet, G. (2023). Live-cell imaging in the deep learning era. Current Opinion in Cell Biology, 85, 102271. https://doi.org/10.1016/j.ceb.2023.102271
Laine, R. F., Heil, H. S., Coelho, S., Nixon-Abell, J., Jimenez, A., Wiesner, T., Martínez, D., Galgani, T., Régnier, L., Stubb, A., Follain, G., Webster, S., Goyette, J., Dauphin, A., Salles, A., Culley, S., Jacquemet, G., Hajj, B., Leterrier, C., & Henriques, R. (2023). High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation. Nature Methods, 20(12), 1949–1956. https://doi.org/10.1038/s41592-023-02057-w
Spahn, C., Middlemiss, S., Gómez-de-Mariscal, E., Henriques, R., Bode, H. B., Holden, S., & Heilemann, M. (2023). Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth [Preprint]. bioRxiv. https://doi.org/10.1101/2023.10.16.562172
Saraiva, B. M., Cunha, I. M., Brito, A. D., Follain, G., Portela, R., Haase, R., Pereira, P. M., Jacquemet, G., & Henriques, R. (2023). NanoPyx: super-fast bioimage analysis powered by adaptive machine learning [Preprint]. Bioinformatics. https://doi.org/10.1101/2023.08.13.553080
Kemmer, I., Keppler, A., Serrano-Solano, B., Rybina, A., Özdemir, B., Bischof, J., El Ghadraoui, A., Eriksson, J. E., & Mathur, A. (2023). Building a FAIR image data ecosystem for microscopy communities. Histochemistry and Cell Biology. https://doi.org/10.1007/s00418-023-02203-7
Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F. A., Ren, T. I., Meyerowitz, E. M., Scherr, T., Löffler, K., Mikut, R., Guo, T., Wang, Y., Allebach, J. P., Bao, R., Al-Shakarji, N. M., Rahmon, G., Toubal, I. E., Palaniappan, K., … Ortiz-de-Solórzano, C. (2023). The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods, 20(7), 1010–1020. https://doi.org/10.1038/s41592-023-01879-y
Volpe, G., Wählby, C., Tian, L., Hecht, M., Yakimovich, A., Monakhova, K., Waller, L., Sbalzarini, I. F., Metzler, C. A., Xie, M., Zhang, K., Lenton, I. C. D., Rubinsztein-Dunlop, H., Brunner, D., Bai, B., Ozcan, A., Midtvedt, D., Wang, H., Sladoje, N., … Bergman, J. (2023). Roadmap on Deep Learning for Microscopy (No. arXiv:2303.03793). arXiv. https://doi.org/10.48550/arXiv.2303.03793
Workshop participants. (2023). AI4Life & BioImage Archive FAIR AI Workshop - Summary of recommendations. https://doi.org/10.5281/zenodo.7681687
Ouyang, W., Eliceiri, K. W., & Cimini, B. A. (2023). Moving beyond the desktop: prospects for practical bioimage analysis via the web. Frontiers in Bioinformatics, 3. https://www.frontiersin.org/articles/10.3389/fbinf.2023.1233748
Ouyang, W., Beuttenmueller, F., Gómez-de-Mariscal, E., Pape, C., Burke, T., Garcia-López-de-Haro, C., Russell, C., Moya-Sans, L., de-la-Torre-Gutiérrez, C., Schmidt, D., Kutra, D., Novikov, M., Weigert, M., Schmidt, U., Bankhead, P., Jacquemet, G., Sage, D., Henriques, R., Muñoz-Barrutia, A., … Kreshuk, A. (2022). BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis. https://doi.org/10.1101/2022.06.07.495102
