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
INCISIVE is a 42-month project that aims at exploring the potential of novel AI tools for enhancing current imaging solutions for cancer cases. INCISIVE addresses challenges related to the detection of patterns in large volumes of cancer imaging data, thus increasing the interpretability of complex imaging data and supporting more effective decision-making for Healthcare Providers. It also addresses challenges related to data labelling and annotation, as well as availability and sharing of imaging data so that it can be used for training and validating AI tools for improved imaging methods. The project targets two main results: A) an AI-based toolbox consisting of novel AI models, combined with a set of predictive, descriptive and prescriptive analytics; this includes a Machine Learning (ML-) based automatic annotation system to produce data for the training of algorithms in AI research B) an interoperable pan-European federated repository of health images that allows the donation and sharing of data in compliance with legal, ethical, privacy and security requirements, for AI-related training and experimentation. The project solutions will be piloted in 8 sites, on four types of cancer: lung, colorectal, breast and prostate cancer. Ultimately, INCISIVE aspires to enable more accurate and better informed decisions by improving the sensitivity and specificity of cancer imaging methods, even lower cost methods, increasing their accuracy in cancer diagnosis, prediction, evolution and relapse.
Coordination: Maggioli group
Funding scheme: Horizon 2020, RIA – Research and Innovation action