Objective
Machines capable of analysing and interpreting medical scans with super-human performance would transform healthcare as much as medical imaging itself did over the last century. With an increasing complexity and volume of data the interpretation of images and extraction of clinically useful information push human abilities to the limit. There is high risk that critical patterns of disease go undetected. We require powerful and trustworthy computational tools based on machine intelligence to support experts and go beyond human performance to tackle the major challenges in clinical practice. Two key ingredients are currently missing: 1) interpretable statistical representations that capture important information while reducing complexity; 2) intelligent algorithms that leverage knowledge across multiple tasks to solve the most challenging problems such as early detection of pathology.
This project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. Key to the project is its unique access to some of the largest and most comprehensive imaging databases combined with world-leading expertise in machine learning and medical imaging. An overarching objective is to harvest information from population data to construct what will be the most advanced statistical models of anatomy. In contrast to previous attempts that focus primarily on specific organs or pathology, here shared representations are learned from highly complex data by jointly solving multiple tasks. Linking the representations with demographics, lifestyle, genetics and disease allows probing of genetic and environmental determinants related to specific anatomical and pathological phenotypes across organs. This will provide insights into complex diseases, and enables a novel approach to abnormality detection that aims to automatically find subtle signs of pathology in new medical scans.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences genetics
- natural sciences computer and information sciences databases
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences mathematics applied mathematics statistics and probability
- engineering and technology medical engineering diagnostic imaging
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-STG - Starting Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2017-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
SW7 2AZ London
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.