Project description
Innovative software for medical applications of hyperspectral imaging
Hyperspectral (HS) imaging provides information by generating high-resolution images captured at a wide range of the electromagnetic spectrum. The spectral resolution of HS data enables the identification of subtle spectral differences related to pathological conditions. Medical HS (mHS) applications have been demonstrated for the non-invasive diagnosis of diseases including brain and tongue cancer, as well as for diabetic foot diagnosis and surgical guidance. The EU-funded HyPPOCRATES project aims to create a powerful mHS imaging interpretation software platform for mHS data processing by applying machine-learning principles. Overall, the main objective of the project is to bridge the gap between the recent advances in mHS imaging and data interpretation based on machine learning.
Objective
Over the past few years hyperspectral (HS) imaging has been broadly applied in a wealth of different applications with
remote sensing of the environment being the most prominent one. HS imaging provides a rich amount of information by
generating images and videos of high spectral resolution captured at a wide range of the electro-magnetic spectrum.
Recently, HS data have been shown to offer remarkable advances to a new field of significant interest i.e. medical HS
(mHS) imaging. The high spectral resolution of HS data makes them amenable to identifying even subtle spectral differences
related to various pathological conditions. In view of that, mHS images and videos have received considerable attention
lately. mHS data have already been used for non-invasive diagnosis of several types of cancer e.g. brain, tongue cancer, as
well as for diabetic foot diagnosis and surgical guidance. mHS imaging is anticipated to remarkably flourish in the years to
come taking into account the recent advances that have occurred in the development of micro-size and low-cost HS
cameras. However, despite this large progress in HS imaging hardware, sophisticated algorithms capable to interpret these
data are still missing. HyPPOCRATES aims at deriving new powerful mHS image and video interpretation schemes tailored
to mHS data processing, by applying novel machine learning ideas. To this end, the problems of subspace clustering and
unmixing will be investigated for performing refined mHS image and video understanding. Along those lines, constrained
matrix and tensor factorization approaches will be explored for devising computationally efficient and scalable machine
learning algorithms. Overall, the main objective of the project is to bridge the gap between the recent advances in mHS
imaging and those in machine learning research. This way, the researcher aspires to go the diagnostic process of several
serious diseases, such as various types of cancer, one step further.
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.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors optical sensors
- engineering and technology environmental engineering remote sensing
- medical and health sciences clinical medicine oncology
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences data science data processing
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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.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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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.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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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) H2020-MSCA-IF-2018
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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.
11 810 ATHINA
Greece
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.