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- Title
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.
- Authors
Abrol, Anees; Fu, Zening; Salman, Mustafa; Silva, Rogers; Du, Yuhui; Plis, Sergey; Calhoun, Vince
- 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 comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain. Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) for brain imaging data analysis. Here, the authors show that if trained following prevalent DL practices, DL methods substantially improve compared to SML methods by encoding robust discriminative brain representations.
- Subjects
MACHINE learning; MAGNETIC resonance imaging; BRAIN imaging; COMPUTATIONAL complexity; IMAGE analysis; DEEP learning
- Publication
Nature Communications, 2021, Vol 12, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-020-20655-6