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Maggioli Group leads, as Project Coordinator, an EU flagship project on the development of AI for cancer imaging: the INCISIVE Project

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

Through the Greek Branch specialized in Research and Development, in constant collaboration with the Italian team, Maggioli Group is coordinating the multinational project 'INCISIVE’.

The increase in the amount and availability of health data, especially medical imaging in cancer cases, combined with the emergence of new technological tools based on Artificial Intelligence (AI) and Machine Learning (ML) offer unprecedented opportunities for improving the accuracy of cancer detection, prediction and follow-up, thus allowing clinical care decisions that are better informed.

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:

  • an AI-based toolbox consisting of novel AI models, combined with a set of predictive, descriptive and prescriptive analytics, enabling the multi-modal exploration of the available data sources; this includes a Machine Learning (ML-) based automatic annotation system to produce data for the training of algorithms in AI research;
  • 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 repository will operate on a High Performance Computing as-a-service basis, thereby allowing for cost-effective performance of computationally intensive processing without the need for maintaining expensive equipment.

The project solutions will be piloted on four types of cancer: lung, colorectal, breast and prostate cancer.

The pilot activities will be carried out in 8 countries including Greece, Italy, Spain, Cyprus and Serbia.

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.

INCISIVE has received funding from the European Union’s Horizon 2020 research and innovation programme. The INCISIVE consortium, coordinated by Maggioli SpA, is implemented by 26 organizations from 9 different countries: Italy, Spain, Finland, Greece, Cyprus, Serbia, Belgium, United Kingdom, Luxembourg. The consortium brings together expertise in AI, medical image processing, data analytics, security (INSTITUTE OF COMMUNICATION & COMPUTER SYSTEMS, CENTER FOR RESEARCH AND TECHNOLOGY HELLAS, ARISTOTLE UNIVERSITY OF THESSALONIKI, FUNDACIO TSCALUT, SQUAREDEV, UNIVERSITY OF HELSINKI, UNIVERSITY OF NOVI SAD, CENTRO REGIONALE ICT SCRL, VISARIS D.O.O, MEDTRONIC IBERICA SA), complex ICT systems (MAGGIOLI SpA, TELESTO IOT SOLUTIONS LTD, EUROPEAN DYNAMICS LUXEMBOURG SA, INNOSYSTEMS LTD), High Performance Computing (BARCELONA SUPERCOMPUTING CENTER), cancer clinical research and practice (FUNDACIO CLINIC PER A LA RECERCA BIOMEDICA, LINAC-PET SCAN OPCO LTD, UNIVERSITY OF ROME TOR VERGATA, UNIVERSITY OF NAPLES FEDERICO II, HELLENIC CANCER SOCIETY, CYPRUS ASSOCIATION OF CANCER PATIENTS AND FRIENDS,  UNIVERSITY OF ATHENS, KINGSTON UNIVERSITY, CYPRUS UNIVERSITY OF TECHNOLOGY), legal and ethical issues (TIME.LEX) and innovation and business planning (WHITE RESEARCH).

31 January 2022
Posted: 1/31/2022 10:30:46 AM by | with 0 comments

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