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Functional learning: From theory to application in bioimaging

Project description

Deep neural networks in bioimaging applications

Deep neural networks (DNNs) are computational models where many simple processing units work in parallel in interconnected layers. A DNN performs particular tasks through training, learning the strength of connections between units. DNNs show the capability of improving the quality of biomedical image reconstruction. However, the main objection concerns the difficulty to control the Lipschitz constant of current neural architectures, meaning that a small perturbation of the input can result in a huge deviation of the output, negatively impacting image reconstruction. The EU-funded FunLearn project proposes to address this issue with the use of much shallower networks, which are easier to control. The approach relies on functional optimisation to improve learning architectures, and the development of specific neural networks to solve problems in biomedical imaging.

Host institution

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Net EU contribution
€ 2 665 115,00
Address
Batiment Ce 3316 Station 1
1015 Lausanne
Switzerland

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Region
Schweiz/Suisse/Svizzera Région lémanique Vaud
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00

Beneficiaries (1)

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Switzerland
Net EU contribution
€ 2 665 115,00
Address
Batiment Ce 3316 Station 1
1015 Lausanne

See on map

Region
Schweiz/Suisse/Svizzera Région lémanique Vaud
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00