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UNCARIA

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UNCARIA UNcertainty estimation in CARdiac Image Analysis

Cardiovascular diseases account for nearly 45% of all deaths in Europe, with a yearly cost to the EU economy of €210 billions. The emergence of a new generation of deep neural networks (DNNs), powered by higher computing capabilit... ver más

29/02/2024

UPF

224K€

Presupuesto del proyecto: 224K€

Líder del proyecto

UNIVERSITAT POMPEU FABRA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 322

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-02-29

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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2 Participantes

UPF

224.07K€ | Lider

N.D. | Participante

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Duración del proyecto: 47 meses Fecha Inicio: 2020-03-10
Fecha Fin: 2024-02-29

Líder del proyecto

UNIVERSITAT POMPEU FABRA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 322

Presupuesto del proyecto 224K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Cardiovascular diseases account for nearly 45% of all deaths in Europe, with a yearly cost to the EU economy of €210 billions. The emergence of a new generation of deep neural networks (DNNs), powered by higher computing capabilities and the availability of large amounts of data, has enabled unprecedented predictive accuracy, bringing the promise of improving risk assessment and early diagnosis to the field of computational cardiac image understanding. Unfortunately, clinical translation of these tools has not been effectively accomplished yet. A key reason is the black-box nature of these models: through the observation of large-scale annotated data, DNNs can build rich, complex decision boundaries in the image space, but the sequence of mathematical operations leading to such decisions is not readily interpretable by humans. The goal of this project is to open this black-box in a specific direction: building in these models the ability of understanding when they deliver a prediction with a well-founded confidence degree, and when a prediction is reached based only on local statistical regularities of training data and may not be reliable. Current models largely lack this ability, and this undermines their potential for clinical adoption. This project revolves around a fundamental idea: redefining the conventional way of training DNNs so that they can not only produce accurate diagnostic predictions but also model their own errors and have an awareness of them. This proposal involves the transfer of the candidate to a worldwide renowned computer vision group, with a secondment in a top-tier medical research institution, followed by a returning stage in one of the most prestigious biomedical image analysis research groups within Europe. The proposed workplan is designed to train the candidate in both cutting-edge computer vision and clinical knowledge in the outgoing stage, maximizing potential for knowledge transfer to the European host during the incoming phase.

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