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Few-shot Decoding of Brain Activation Maps

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.

@inproceedings{BonLioFarGri20218,
  author = {Myriam Bontonou and Giulia Lioi and
Nicolas Farrugia and Vincent Gripon},
  title = {Few-shot Decoding of Brain Activation
Maps},
  booktitle = {29th European Signal Processing
Conference (EUSIPCO)},
  year = {2021},
  pages = {1326-1330},
  month = {August},
}