The FLUTE project is set to revolutionize healthcare data utilization through a privacy-preserving approach. Our project aims to improve predictions of aggressive prostate cancer through AI support to physician, while minimizing unnecessary biopsies, ultimately benefiting patients and reducing associated costs.
Define the future users' needs for the FLUTE platform.
Enhance Federated Learning scalability to empower privacy and performance.
Develop a novel method for generating multi-modal synthetic health data.
Establish the FLUTE platform with robust privacy standards.
Discover a Federated Learning solution to aid physicians in diagnosing prostate cancer.
Contribute to the standardization of HL7 FHIR.
Formulate comprehensive guidelines for GDPR-compliant cross-border Federated Learning in healthcare.
Promote FLUTE outcomes within relevant ecosystems and communities.
Ensure the long-term sustainability of project outcomes.
Why Federated Learning in Healthcare?
Federated learning represents a machine learning technique designed to train models on decentralized data, eliminating the necessity of central data collection and aggregation. This approach retains data on originating devices or servers while utilizing aggregated gradients from numerous devices to train the model...
AI in Prostate Cancer
Why AI in Prostate Cancer?
In recent times, magnetic resonance imaging (mpMRI) has become a prevalent tool for the early detection of clinically significant Prostate Cancer (csPCa). While assessing the aggressiveness of prostate cancer predominantly relies on the traditional Gleason grading system introduced in 1974, FLUTE aims to simplify...