Project description
Advancing AI-powered medical imaging
The rapid advancement of AI has revolutionised numerous fields, from image recognition to medical diagnostics. Deep learning (DL) plays a crucial role in this progress, but optimising its performance remains a challenge. In medical imaging, AI-powered devices have the potential to improve diagnostic accuracy and ultimately save lives. However, current imaging techniques lack the depth and detail needed for truly transformative impact. The P3D Endoscopy project builds on breakthroughs from the ERC-funded SPADE project to enhance medical imaging. By integrating intelligent optical imaging methods, it introduces reliable 3D and polarisation data into endoscopic devices. This innovation promises clearer, more precise imaging, empowering physicians to make more accurate diagnoses and offer high-quality care to a broader population.
Objective
The proliferation of artificial intelligence (AI) has led to a major advancement in many fields. It has enabled achieving non-precedent achievements such as human-level image recognition, intelligible language translation, and accurate medical diagnosis. The main research goal of the PIs ERC-StG SPADE project is to use tools for data modelling to improve our understanding and usage of deep learning (DL) and thus enhance its performance in medical problems and computational imaging [1-4]. One of the fields that could greatly benefit from such progress is sensitive medical treatment, using AI-based medical devices. Such devices can initially assist the physicians with a more accurate diagnosis, and potentially save lives. As such technology matures, the information provided to the physicians will be richer, thus, leading to more accurate diagnoses to the cases at hand. Such advancement will allow high quality and efficient treatment to a much wider public. The scope of this POC is to apply intelligent optical imaging methods developed in the SPADE project, specifically, for introducing reliable 3D and polarization information to medical imaging devices.
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.
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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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HORIZON.1.1 - 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.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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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-2022-POC2
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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.
69978 Tel Aviv
Israel
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.