Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising

Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam

Abstract: Highlights•We propose a unified deep attentive CNN framework for automatic rs-fMRI denoising.•It simultaneously learns spatio-temporal features of noise in a data-driven manner.•We provide visual explanations to depict how the CNNs work for noise detection.•It achieves high performance on various datasets including infant cohorts.•It can be integrated into any pipelines by accelerating speed (<<math><mo is="true">&lt;</mo></math>1s per component).