Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

ABSTRACT: Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage - representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comp

SUBMITTER: Abrol A 

PROVIDER: S-EPMC7806588 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature