Grants & Awards

We are proud to participate in granted projects and with remarkable institutions and collaborators that allow us to continue our research and contribute to the advancement of artificial intelligence and imaging biomarkers for the improvement of people’s healthcare.

Granted studies in RWE and imaging


    EUCAIM new logo

    The EUropean Federation for CAncer IMages (EUCAIM) project originates from an unprecedented body of work and expertise of the “AI for Health Imaging” Network (AI4HI), which consists of 86 affiliated institutions from 20 countries involved in 5 large EU-funded projects on big data and AI in cancer imaging (CHAIMELEON, EUCANIMAGE, INCISIVE, ProCancer-I, PRIMAGE; coordinated by HULAFE, UB, MAG, FORTH and HULAFE, respectively). This network will bring information from more than 100.000 patients with cancer. The AI4HI Network has been organised into 8 working groups (Ethical and legal issues, Metadata interoperability, Data storage and management, Data annotation, AI development, AI validation, Clinical Working Group and Outreach Working Group).

    Commited patient data: 100.000.

    Amount awarded to Quibim: 1.475.682,48€.

    More info.

    Co-founded EU

    EUCAIM is co-funded by the European Union under Grant Agreement number 1011100633.


    Chaimeleon Logo


    CHAIMELEON aims to set up a structured repository for health imaging data to be openly reused in AI experimentation for cancer management. An EU-wide repository will be built as a distributed infrastructure in full compliance with legal and ethics regulations in the involved countries. It will build on partner ́s experience (e.g. PRIMAGE repository for paediatric cancer and the Euro-BioImaging node for Valencia population, by HULAFE; the Radiomics Imaging Archive by Maastricht University; the national repository DRIM AI France, the Oncology imaging biobank by Pisa University). Clinical partners and external collaborators will populate the Repository with multimodality (MR, CT, PET/CT) imaging and related clinical data for historic and newly diagnosed lung, prostate and colorectal cancer patients.

    Commited patient data: 39.000.

    Amount awarded to Quibim: 458.750€.

    More info.

    UE Flag

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952172.

  • ProCAncer-I

    ProCancerI logo


    The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (>17,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and neutral AI models for addressing 8 PCa clinical scenarios.

    Commited patient data: 17.000.

    Amount awarded to Quibim: 400.000€.

    More info.

    UE Flag

    ProCAncer-I has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952159.


    PRIMAGE logo


    PRIMAGE proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumours, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow up, based on the use of novel imaging biomarkers, in-silico tumour growth simulation, advanced visualisation of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points.

    Commited patient data: 3.300.

    Amount awarded to Quibim: 919.063,85€.

    More info.

    UE Flag

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826494.

  • PainFACT

    PainFact Logo

    Chronic pain (CP) is the leading cause of disability, and is strongly associated with fatigue, anxiety and depression ─ also major contributors to disability, and with cardiovascular disease (CVD) and mortality. Twin studies indicate that these associations are a consequence of common causal mechanisms. The main objective of this PainFACT is to identify these mechanisms. Using hypothesis-free genomic, proteomic and metabolomics discovery in available human studies, as well as mining of existing data from mice, we aim to identify biomarkers that are associated across conditions.

    Commited patient data: 9.850.

    Amount awarded to Quibim: 250.392,5€.

    More info.

    UE Flag

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 848099.

  • ProCanAid

    ProCanAid logo

    The aim of the ProCanAid project is to develop a computational tool to create a 4D digital twin of the entire prostate of a patient. Novel AI-based magnetic resonance imaging segmentation algorithms will be applied to extract not only patient-specific prostate anatomy (transitional zone, peripheral zone, seminal vesicles, and neurovascular bundle) but also to detect PCa. The digital twin will incorporate in silico models considering the behavior of cells and tissues, to predict the effects of different types of oncological treatments not only on the tumor but also on the entire prostate, as well as to predict the efficacy of these treatments and the possible evolution of the disease. Quibim leads this project as the coordinator entity.

    Commited patient data: 500.

    Amount awarded to Quibim: 791.954,6€.

    More info.

    Project PLEC2021-007709 funded by MCIN/AEI/10.13039/501100011033 and by Unión Europea NExtGenerationEU/PRTR

    Logos CMIN UE

    DIPCAN logo

    The main objective of the DIPCAN Project for Digitalization and Comprehensive Management of Personalized Medicine in CANcer (MIA.2021.M02.0006) is to develop an algorithm based on artificial intelligence methods that allows to guide and objectively help making decisions regarding the clinical management of the patient. The objectives of the DIPCAN project they also include the effective prediction of tumor genetics, as well as the risk of a certain type of metastasis. In order to carry out, interdisciplinary and coordinated action and integrated analysis of the phenotypic / clinical, pathological, radiomic and genetic data of patients with metastatic cancer will be necessary.

    Commited patient data: 2.000.

    Amount awarded to Quibim: 674.745,24€.

    More info.

    Logo UE y Plan resiliencia
  • RadioVal

    RadioVal logo

    RadioVal is the first multi-centre, multi-continental and multi-faceted clinical validation of radiomics-driven estimation of NAC response in breast cancer. The project builds on the repositories, tools and results of five EU-funded projects from the AI for Health Imaging (AI4HI) Network. To test applicability as well as transferability, the validation with take place in eight clinical centres from three high-income EU countries (Sweden, Austria, Spain), two emerging EU countries (Poland, Croatia), and three countries from South America (Argentina), North Africa (Egypt) and Eurasia (Turkey). RadioVal will develop a comprehensive and standardised methodological framework for multi-faceted radiomics evaluation based on the FUTURE-AI Guidelines, to assess Fairness, Universality, Traceability, Usability, Robustness and Explainability. Furthermore, the project will introduce new tools to enable transparent and continuous evaluation and monitoring of the radiomics tools over time.

    Commited patient data: >6.000.

    Amount awarded to Quibim: 412.500€.

    More info.

    Funded EU

    This work received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101057699 (RadioVal project).


    Flute Logo

    The FLUTE project will advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments. The technical innovations will be integrated in a privacy-enforcing platform that will provide innovators with a provenly secure environment for federated healthcare AI solution development, testing and deployment, including the integration of real world health data from the data hubs and the generation and utilization of synthetic data. To maximize the impact, adoption and replicability of the results, the project will contribute to the global HL7 FHIR standard development, and create novel guidelines for GDPR-compliant cross-border Federated Learning in healthcare.

    Commited patient data: 6.000 (to be finally determined).

    Amount awarded to Quibim: 530.000€.

    More info.

    Funded EU

    This project has been funded by the European Union under Grant Agreement number 101095382.

Other funding

Check here the funding granted to us by public institutions.