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Communication Dans Un Congrès Année : 2021

Few-Shot Decoding of Brain Activation Maps

Résumé

Few-shot learning addresses problems for which a limited number of training examples are available. So far, the field has been mostly driven by applications in computer vision. Here, we are interested in adapting recently introduced few-shot methods to solve problems dealing with neuroimaging data, a promising application field. To this end, we create a neuroimaging benchmark dataset for few-shot learning and compare multiple learning paradigms, including meta-learning, as well as various backbone networks. Our experiments show that few-shot methods are able to efficiently decode brain signals using few examples, which paves the way for a number of applications in clinical and cognitive neuroscience, such as identifying biomarkers from brain scans or understanding the generalization of brain representations across a wide range of cognitive tasks.

Dates et versions

hal-03675137 , version 1 (22-05-2022)

Identifiants

Citer

Myriam Bontonou, Giulia Lioi, Nicolas Farrugia, Vincent Gripon. Few-Shot Decoding of Brain Activation Maps. EUSIPCO 2021: 29th European Signal Processing Conference, Aug 2021, Dublin, Ireland. pp.1326-1330, ⟨10.23919/EUSIPCO54536.2021.9616158⟩. ⟨hal-03675137⟩
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