Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

Abstract

Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience [1][2][3][4] . Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data [5][6][7][8] .Many of the impressive results obtained in CAD using deep learning (DL) techniques utilize mainly image datasets. DL networks typically require annotations of several images for employing supervised learning techniques and are one of the roadblocks in employing these state of the art networks in various classification tasks in MRI/fMRI. However, unsupervised learning techniques within the DL paradigm are now being employed in natural image classification with a lot of success and we believe the adaptability of these to the neuroimaging data are required to attack challenging neuroimage analysis problems.A stacked denoising auto encoders approach that is an unsupervised learning technique was used [9] for brain tumor segmentation in MRI imagery. The experimental results showed that using this particular approach is as good as using supervised learning based DL techniques that require accurate image-based annotations. This indicates that we can use different unsupervised learning in DL networks variants for

Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network

“…automatically, and we can not only extract feature descriptors from the given images but also construct a classifier for the problem automatically at the same time. Deep learning is currently popular in the field of computer vision and pattern recognition [19,20] and in particular for computer assisted biomedical image analysis [21,22]. Also, it showed outstanding performance in solving various biomedical image analysis problems [23,24].…”