INCISIVE aims to develop and validate an AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods The project’s objective to enhance cancer diagnosis and prediction using AI and big data will be reached by implementing work within 5 project Pillars. 1. Analysis of AI challenges related to cancer imaging while striving to achieve a highly acceptable solution 2. Incorporation of AI features enhancing cancer imaging and enabling effective decision making 3. Implementation of an interoperable pan-European federated repository of health images, including secure data sharing mechanisms 4. System validations, technology assessment and proof-of-concept demonstration 5. Active engagement of stakeholders, improving user acceptance and resulting to measurable impacts

Project information


A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer


Gianna Tsakou (Maggioli SpA)

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