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CORDIS - EU research results
CORDIS

Synthetic and scalable data platform for medical empowered AI

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

Dissemination and Communication Plan – Version 1.0 (opens in new window)

Core document outlining the impact-enabling approach, activities, channels (website and social media profiles), tools and timing at the basis of the project’s D&C outreaching strategy, the internal communication processes, templates, responsibilities, KPIs and operative plan for action.

Project website (opens in new window)

Project public website for dissemination of project news events and results and connection with project and partners social medias

Piloting planning and monitoring approach (opens in new window)

Document gathering all the requirements needed to guarantee appropriate implementation of the use cases and management of users’ data. This document will include the definition of the legally and ethically compliant engagement process for each end-user, modules/ formats/ checklist/ guidelines for the correct deployment and usage of the developed modules in the platform experimentation. In addition, specific objectives and KPIs for each testing iteration will be defined, as well as the specific timing, minimal configurations (e.g., technical configuration, number, approach for feedback collections, etc.) and tools (e.g. questionnaires, comparison approaches) for KPIs measurement.

State of the Art Report (Suntheti data generation) (opens in new window)

Report comprising an in-depth survey of the SOA approaches that have been developed for synthetic data generation, providing a strong basis that will guide the research conducted during the project.

State of the art report (Data model auditing) (opens in new window)

Analysis of the state of the art for data model auditing, including complete literature review, scouting of relevant R&I projects’ results as well as market solution, and selection of most relevant contributions to be used for AISym4Med (e.g. model/algorithm and how to be applied within the platform)

Legal and ethical requirements report and updates (opens in new window)

Analysis of legal references relevant for the platform ethical assessment and list of mandatory requirements and corresponding strategies to implement them including in annex any templateformat needed eg informed consent model and matchmaking of these requirements with the corresponding SW components

Publications

synple: A Platform for Privacy Preserving Synthetic Patient Data Generation (opens in new window)

Author(s): Silveira, I., Silva. L., Veladas. F, Braga, R. & Gamboa, H.
Published in: 2024, ISBN 978-3-031-63851-0
Publisher: Cham: Springer Nature Switzerland
DOI: 10.1007/978-3-031-63851-0_9

Systematic analysis of the impact of label noise correction on ML Fairness (opens in new window)

Author(s): Silva, I. Oliveira e; Soares, C.; Sousa, I.; Ghani, R.
Published in: 2023, ISBN 978-981-99-8391-9
Publisher: Springer, Singapore
DOI: 10.48550/arxiv.2306.15994

Adapting Stable Diffusion Models for Domain-Specific Medical Imaging: A Case Study in Synthetic Retinal Fundus Image Generation (opens in new window)

Author(s): Façoco, Ivo; Mesquita, Gonçalo; Lúcio, Francisca; Rosado, Luís
Published in: Proceedings of the ECML-PKDD workshop SynDAiTE: Synthetic Data for AI Trustworthiness and Evolution, 2025
Publisher: Springer
DOI: 10.5281/ZENODO.17207258

GASTeN: Generative Adversarial Stress Test Networks (opens in new window)

Author(s): Cunha, L., Soares, C., Restivo, A., Teixeira, L.F.
Published in: 2023, ISBN 978-3-031-30047-9
Publisher: Cham: Springer Nature Switzerland
DOI: 10.1007/978-3-031-30047-9_8

Kernel Corrector LSTM (opens in new window)

Author(s): Tuna, R., Baghoussi, Y., Soares, C., Mendes-Moreira, J.
Published in: 2024
Publisher: Cham: Springer Nature Switzerland.
DOI: 10.1007/978-3-031-58553-1_1

Designing for Qualitative Evaluation of Synthetic Medical Data (opens in new window)

Author(s): Isabella Barbosa Silva; Elsa Oliveira; Ricardo Melo; Luís Rosado; César Gálvez-Barrón; Irene Bernadet Heijink; Sem Hoogteijling; Iñigo Gabilondo
Published in: CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2025
Publisher: ACM
DOI: 10.1145/3706599.3720274

Benchmarking deep neural representations for synthetic data evaluation (opens in new window)

Author(s): Nuno, Bento; Joana, Rebelo; Marilia, Barandas
Published in: Intelligent Systems with Applications, 2025, ISSN 2667-3053
Publisher: ScienceDirect
DOI: 10.1016/J.ISWA.2025.200580

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