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MALIN

Financiado

Model-aware learning for imaging inverse problems in fluorescence microscopy

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 represen... ver más

31/10/2029

UNIGE

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

UNIVERSITA DEGLI STUDI DI GENOVA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-14

ERC-2023-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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1 Participantes

UNIGE

1.43M€ | Lider

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Duración del proyecto: 67 meses Fecha Inicio: 2024-03-14
Fecha Fin: 2029-10-31

Líder del proyecto

UNIVERSITA DEGLI STUDI DI GENOVA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto 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.

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