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CORDIS - EU research results
CORDIS

Open Consortium for Decentralized Medical Artificial Intelligence

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Source code for the web front-end (opens in new window)

The complete source code for the web front-end, shared publicly and open access

Project Video (opens in new window)

First short video/animation to raise awareness of the ODELIA project

Updated web front-end (opens in new window)

Updated web front-end (version 2.0) connected to a back-end AI-processing pipeline (can display the results of all ODELIA AI models or uploaded models on a fixed set of data or data uploaded by the user)

Web front-end (opens in new window)

A web front-end with basic functionalities (version 1.0, can display the saved results of an AI model for a fixed set of data) will be operational and publicly usable by any stakeholder, including citizen scientists

Manual on setting up SL onsite (opens in new window)

Manual describing how to set up SL onsite

Trained neural networks for detection of breast cancer in MRI images (including models trained on local data only and models trained in the swarm) (opens in new window)

Trained neural networks for detection of breast cancer in MRI images. This includes trained neural network models trained on local data only and models trained in the swarm.

Report comparing the performance of baseline AI model against the winners of the competition, trained locally and in SL (opens in new window)

Report comparing the performance of baseline AI model against the winners of the competition. This included neural networks trained locally and in SL.

Communication kit including website (opens in new window)

Communication kit for ODELIA, including communication, dissemination and design assets such as a presentation template, standard slides introducing the project, a short project description, the logo and related visual assets, and the project website and online presence.

Report on summer schools (opens in new window)

Report summarising the different summer schools organised by ODELIA

Midterm recruitment report (opens in new window)

This report is due when 50% of the study population is recruited. The report includes an overview of the number of recruited participants by clinical sites, any problems in recruitment and, if applicable, a detailed description of implemented and planned measures to compensate for any incurred delays.

Guidance document on running SL in clinical environments (opens in new window)

Guidance document for partners describing how to run SL in clinical environments

Instructions on training AI models with SL in a tangible demonstration case (opens in new window)

Report with instructions on how to train AI models with SL based on a tangible demonstration case

Report comparing the performance of locally trained models against swarm-trained models in breast cancer screening (opens in new window)
Study initiation package (opens in new window)

Study initiation package (before enrolment of the first study participant)This deliverables includes:- Registration number of the clinical study in a registry meeting WHO Registry criteria- Final version of study protocol as approved by the regulator(s) / ethics committee(s)- Regulatory and ethics approvals required for the enrolment of the first study participant

Publications

meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis (opens in new window)

Author(s): Dishantkumar Sutariya, Eike Petersen
Published in: Lecture Notes in Computer Science, Fairness of AI in Medical Imaging, 2025
Publisher: Springer Nature Switzerland
DOI: 10.1007/978-3-032-05870-6_19

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging (opens in new window)

Author(s): Oliver Lester Saldanha; Jiefu Zhu; Gustav Müller-Franzes; Zunamys I. Carrero; Nicholas R. Payne; Lorena Escudero Sánchez; Paul Christophe Varoutas; Sreenath Kyathanahally; Narmin Ghaffari Laleh; Kevin Pfeiffer; Marta Ligero; Jakob Behner; Kamarul A. Abdullah; Georgios Apostolakos; Chrysafoula Kolofousi; Antri Kleanthous; Michail Kalogeropoulos; Cristina Rossi; Sylwia Nowakowska; Alexandra Athanasiou; Raquel Perez-Lopez; Ritse Mann; Wouter Veldhuis; Julia Camps; Volkmar Schulz; Markus Wenzel; Sergey Morozov; Alexander Ciritsis; Christiane Kuhl; Fiona J. Gilbert; Daniel Truhn; Jakob Nikolas Kather
Published in: Communications Medicine, Issue 5, 38 (2025), 2025, ISSN 2730-664X
Publisher: Springer Nature
DOI: 10.18154/RWTH-2025-10188

Large language models could make natural language again the universal interface of healthcare (opens in new window)

Author(s): Jakob Nikolas Kather; Dyke Ferber; Isabella C. Wiest; Stephen Gilbert; Daniel Truhn
Published in: Nature Medicine, 2024, ISSN 1546-170X
Publisher: Springer Nature
DOI: 10.1038/S41591-024-03199-W

Overcoming regulatory barriers to the implementation of AI agents in healthcare (opens in new window)

Author(s): Oscar Freyer; Sanddhya Jayabalan; Jakob N. Kather; Stephen Gilbert
Published in: Nature Medicine, 2025, ISSN 1546-170X
Publisher: Springer Nature
DOI: 10.1038/S41591-025-03841-1

Scientific Reports (opens in new window)

Author(s): Müller-Franzes, Gustav; Khader, Firas; Siepmann, Robert; Han, Tianyu; Kather, Jakob Nikolas; Nebelung, Sven; Truhn, Daniel
Published in: Scientific Reports, Issue (2025) 15:23979, 2025, ISSN 2045-2322
Publisher: Springer Nature
DOI: 10.18154/RWTH-2025-06979

Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining (opens in new window)

Author(s): Tianyu Han; Laura Zigutyte; Luisa Huck; Marc Huppertz; Robert Siepmann; Yossi Gandelsman; Christian Blüthgen; Firas Khader; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
Published in: Cell Reports Medicine, 2024, ISSN 2666-3791
Publisher: Cell Press (an imprint of Elsevier)
DOI: 10.48550/ARXIV.2309.17123

Nature Communications (opens in new window)

Author(s): Soroosh Tayebi Arasteh; Tianyu Han; Mahshad Lotfinia; Christiane Kuhl; Jakob Nikolas Kather; Daniel Truhn; Sven Nebelung
Published in: Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024), 2024, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.48550/arxiv.2308.14120

Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging (opens in new window)

Author(s): Soroosh Tayebi Arasteh; Alexander Ziller; Christiane Kuhl; Marcus Makowski; Sven Nebelung; Rickmer Braren; Daniel Rueckert; Daniel Truhn; Georgios Kaissis
Published in: Communications Medicine, 2024, ISSN 2730-664X
Publisher: Springer Nature
DOI: 10.1038/S43856-024-00462-6

Scientific Reports (opens in new window)

Author(s): Firas Khader; Gustav Müller-Franzes; Soroosh Tayebi Arasteh; Tianyu Han; Christoph Haarburger; Maximilian Schulze-Hagen; Philipp Schad; Sandy Engelhardt; Bettina Baeßler; Sebastian Foersch; Johannes Stegmaier; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
Published in: Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023), 2023, ISSN 2045-2322
Publisher: Nature Publishing Group
DOI: 10.18154/rwth-2023-05893

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI (opens in new window)

Author(s): Gustav Müller-Franzes; Luisa Huck; Maike Bode; Sven Nebelung; Christiane Kuhl; Daniel Truhn; Teresa Lemainque
Published in: European Radiology Experimental, 2024, ISSN 2509-9280
Publisher: SpringerOpen
DOI: 10.1186/S41747-024-00451-3

Nature Communications (opens in new window)

Author(s): Jan Clusmann; Dyke Ferber; Isabella C. Wiest; Carolin V. Schneider; Titus J. Brinker; Sebastian Foersch; Daniel Truhn; Jakob Nikolas Kather
Published in: Nature Communications, 2025, ISSN 2041-1723
Publisher: Springer Nature
DOI: 10.1038/S41467-024-55631-X

A European Multi-Center Breast Cancer MRI Dataset

Author(s): Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne, Alexandra Athanasiou, Michael Kalogeropoulos, Aitor Lopez, Alfredo Miguel Soro Busto, Julia Camps Herrero, Nika Rasoolzadeh, Tianyu Zhang, Ritse Mann, Debora Jutz, Maike Bode, et al
Published in: arXiv, Issue 2506.00474, ISSN 2331-8422
Publisher: Cornell University

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