Deep learning for neuroimaging: a validation study

Abstract

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

A survey on deep learning in medical image analysis

“…The first papers applying these techniques for exam classification appeared in 2013 and focused on neuroimaging. Brosch and Tam (2013), Plis et al (2014), Suk and Shen (2013), and Suk et al (2014) applied DBNs and SAEs to classify patients as having Alzheimer's disease based on brain Magnetic Resonance Imaging (MRI). Recently, a clear shift towards CNNs can be observed.…”

Deep Learning in Neuroradiology

“…39 Trained on 80 cases and tested on 20 cases, they showed classification accuracies of Ͼ98%. Plis et al 40 examined both structural and functional MR imaging as input to deep networks for predicting various neurologic diseases. For structural imaging, they showed that they could distinguish patients with schizophrenia and Huntington disease from healthy subjects.…”

Identifying Connectivity Patterns for Brain Diseases via Multi-side-view Guided Deep Architectures

“…Deep learning has also been studied in the diagnosis of brain disease [3,15,16,19]. However, all these existing methods focus on neuroimages and ignore rich information from multiple side views.…”

“…The potential of mining the vast image data for the detection of alterations in neurological disorders has been demonstrated in many studies [3,8,15,16,19,24]. However, connectivities of brain regions are not investigated in these studies.…”