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CORDIS - Risultati della ricerca dell’UE
CORDIS

Synthetic and scalable data platform for medical empowered AI

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Dissemination and Communication Plan – Version 1.0 (si apre in una nuova finestra)

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 (si apre in una nuova finestra)

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

Piloting planning and monitoring approach (si apre in una nuova finestra)

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) (si apre in una nuova finestra)

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) (si apre in una nuova finestra)

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 (si apre in una nuova finestra)

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

Pubblicazioni

synple: A Platform for Privacy Preserving Synthetic Patient Data Generation (si apre in una nuova finestra)

Autori: Silveira, I., Silva. L., Veladas. F, Braga, R. & Gamboa, H.
Pubblicato in: 2024, ISBN 978-3-031-63851-0
Editore: 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 (si apre in una nuova finestra)

Autori: Silva, I. Oliveira e; Soares, C.; Sousa, I.; Ghani, R.
Pubblicato in: 2023, ISBN 978-981-99-8391-9
Editore: 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 (si apre in una nuova finestra)

Autori: Façoco, Ivo; Mesquita, Gonçalo; Lúcio, Francisca; Rosado, Luís
Pubblicato in: Proceedings of the ECML-PKDD workshop SynDAiTE: Synthetic Data for AI Trustworthiness and Evolution, 2025
Editore: Springer
DOI: 10.5281/ZENODO.17207258

GASTeN: Generative Adversarial Stress Test Networks (si apre in una nuova finestra)

Autori: Cunha, L., Soares, C., Restivo, A., Teixeira, L.F.
Pubblicato in: 2023, ISBN 978-3-031-30047-9
Editore: Cham: Springer Nature Switzerland
DOI: 10.1007/978-3-031-30047-9_8

Kernel Corrector LSTM (si apre in una nuova finestra)

Autori: Tuna, R., Baghoussi, Y., Soares, C., Mendes-Moreira, J.
Pubblicato in: 2024
Editore: Cham: Springer Nature Switzerland.
DOI: 10.1007/978-3-031-58553-1_1

Designing for Qualitative Evaluation of Synthetic Medical Data (si apre in una nuova finestra)

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

Benchmarking deep neural representations for synthetic data evaluation (si apre in una nuova finestra)

Autori: Nuno, Bento; Joana, Rebelo; Marilia, Barandas
Pubblicato in: Intelligent Systems with Applications, 2025, ISSN 2667-3053
Editore: ScienceDirect
DOI: 10.1016/J.ISWA.2025.200580

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