Large-Scale Unsupervised Deep Representation Learning for Brain Structure
By Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul M. Thompson. 2018
5 citations recorded by the source corpus.
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of such highly specialized features requires a considerable amount of time, manual oversight and careful quality control to ensure the absence of errors in the computational process. Recent advances in Deep Representation Learning have shown great promise in extracting highly non-linear and information-rich features from data. In this paper, we present a novel large-scale deep unsupervised approach to learn generic feature representations of structural brain MRI scans, which requires no specialized domain knowledge or manual intervention. Our method produces low-dimensional representations of brain structure, which can be used to reconstruct brain images with very low error and exhibit performance comparable to FreeSurfer features on various classification tasks.
Subfields: Biophysics, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging
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In Biophysics
Citations in the corpus
- Unsupervised Adversarial Invariance (2018, background): “The most popular approach to expand the data size and prevent overfitting in deep learning has been synthetic data augmentation [3, 5, 9–11], where multiple copies of data samples are created by altering variations of certain known nuisance factors.”