EuCanImage

EuCanImage will build and demonstrate a GDPR-compliant and scalable platform for leveraging large-scale, high-quality and interoperable cancer imaging datasets adequately linked to biological and health cancer data. The platform will integrate advanced capabilities and new standards to develop and validate integrative decision support systems for precision oncology with increased clinical trust and adoption. The project consortium is an experienced and ambitious academic-industrial-clinical partnership, with a proven track record in data management, responsible data sharing, cancer imaging research, and AI for personalised medicine.

The key objectives are to:

Objective 1: Build a FAIR (Findable, Accessible, Inter-operable, Re-usable) cancer imaging platform linked to biological and health repositories for integrated multi-scale AI in clinical oncology.

Objective 2: Provide comprehensive and user-friendly data curation, annotation, and hosting tools, as well as training material, to promote future data deposition and scalability of the platform.

Objective 3: Build a multi-centre and multi-scale AI development platform for cancer imaging by leveraging the unique expertise of consortium members in radiomics, distributed learning and interpretable AI.

Objective 4: Build an AI assessment and benchmarking platform for multi-disciplinary and clinically-driven evaluation of image-based AI solutions for oncology care.

Objective 5: Develop the legal framework, as well as innovative solutions, that will enable responsible data sharing and enhanced Open Science within EuCanImage and the cancer research community.

Objective 6: Develop a platform that will ultimately contribute to addressing currently unmet clinical needs in personalised cancer care.

Objective 7: Disseminate the EuCanImage platform at large to create the largest community of data contributors as well as AI developers, by leveraging the consortium’s extensive channels and partnering associations.