Objective
This project will develop model-aware, i.e. physics-informed, learning methods for solving imaging inverse problems (IIPs) in fluorescence microscopy imaging (FMI). IIPs are frequently encountered in FMI whenever a visual representation of a biological sample needs to be reconstructed from incomplete and noisy input measurements. Such IIPs are typically ill-posed: their solution (if it exists) is unstable to perturbations. Classical model-based approaches reformulate the IIP at hand as an energy minimisation task. Such approaches rely both on the (approximate) knowledge of the complex physical processes involved and on the mathematical design of hand-crafted optimisation methods whose tuning is often very time-consuming. Concurrently, the impressive development of machine and deep learning methods has enabled the applied imaging community with new data-driven methodologies providing unprecedented results in tasks such as image classification. The performance of data-driven methods for solving IIPs in FMI, however, is halted by their intrinsic unstable behaviour. In MALIN, I propose an integrative paradigm where the stable performance of model-based approaches is combined with the effectiveness of data-driven techniques by means of shallow model-constrained learning and deep physics-informed generative approaches. The reliability of the model-aware methods proposed will be justified by theoretical results providing reconstruction and convergence guarantees. The study will further account for possible geometric invariances and imperfect physical modelling, showing robustness to modelling errors which are frequent when standard (low-cost) equipment is used. Algorithmic acceleration strategies and inexact/stochastic algorithms will be devised to guarantee efficient performance also under limited computational resources and training data. The methodologies will be deployed on several IIPs in FMI and democratised through the release of open software and plug-ins.
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 computer and information sciences software
- natural sciences physical sciences optics microscopy
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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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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 - HORIZON ERC 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-2023-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.
16126 GENOVA
Italy
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.