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 cancerSEE MORE
INCISIVE is a 42-months research project, funded under the call DT-TDS-05-2020 – AI for Health Images.
The INCISIVE project aims to produce the following main outputs: a) An AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods, including an automated Machine Learning (ML)-based image annotation mechanism to rapidly produce training data for machine learning research
b) An interoperable pan-European federated repository of medical images that will enable the secure donation and sharing of data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets and enabling experimentation of AI-based solutions, towards the large-scale adoption of such solutions in cancer diagnosis, prediction and follow-up.
In its lifetime, INCISIVE will make use of multimodal data sources, including imaging, biological and Electronic Health Record (EHR).
INCISIVE will develop a cost-efficient solution incorporating novel AI models, combined with a set of predictive, descriptive and prescriptive analytics, enabling the multi-modal exploration of the available...
The good performance and sustainable exploitation of the INCISIVE AI and analytics components (Pillar 2) are closely linked with the availability and quality of imaging data, as well as their accurate and ea...
This meeting would follow definition of the base elements for the project and other project planning activities+ INFO
International Conference on Public Health Informatics Management+ INFO
In the last 20 years, the worlds of medicine and of information technologies have completely changed...+ INFO
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