A Review of Feature Reduction Techniques in Neuroimaging

By Benson Mwangi, Tian Siva Tian, Jair C. Soares. 2013

548 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Molecular Biology

In Neurology

Citations in the corpus

Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “The most common approach is to use regression analysis or machine learning tools to generate population average maps, which highlights features that are salient across the population [3, 32, 61, 16, 39, 45, 14].”
Reconstructing subject‐specific effect maps (2017, methodology): “nt, maps indicate statistical relationship between the condition and measurement across the entire population. Either a group analysis technique [2,22,40] or a machine learning based predictive model [1,14,26,31,13] is used to compare two cohorts of subjects, one showing the condition of interest an”
Radiomics with artificial intelligence: a practical guide for beginners. (2019, methodology): “The dimension reduction can be done using different approaches such as feature reproducibility analysis (58), collinearity analysis (9), algorithm-based feature selection (57, 59), and cluster analysis.”
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (2016, methodology): “Predictive modeling approaches use machine-learning tools to identify measurements that are highly informative for diagnosing the condition [9,10,11,12].”
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020, background): “This is the so-called “ curse of dimensionality ” in machine learning 52,53 .”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, methodology): “The performance of machine learning algorithms in CAD generally depends on data representation (or feature), and therefore feature extraction becomes a critical step in the classification framework [10], [19].”
Computational psychiatry as a bridge from neuroscience to clinical applications (2016, methodology): “…related provide little independent information, and such redundancies can be identified and removed using general-purpose unsupervised methods, such as principal or independent component analysis, factor analysis or k -nearest neighbor 143,144 , or approaches specific to the data at hand 145 .”
An unsupervised convolutional neural network-based algorithm for deformable image registration (2018, methodology): “These methods tend to suffer from the curse-of-dimensionality when the number of predictor variables is large, which can render K-means and Gaussian mixtures models cumbersome to use for high dimensional medical images (Mwangi et al 2014).”
SubCMap: Subject and Condition Specific Effect Maps (2017, unknown): “In the first approach, models identify a set of measurements that hold predictive information with regards to the condition during training [18, 19, 20].”
Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “, participants, trials), increasing the risk of overfitting (see Rule #1) and complicating model interpretation (Mwangi et al., 2014; Varoquaux et al., 2017).”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, unknown): “…(Mourao-Miranda et al., 2005; Kriegeskorte et al., 2006; Menze et al., 2009; Sabuncu and Van Leemput, 2012; Langs et al., 2011; Yamashita et al., 2008; Gaonkar and Davatzikos, 2012; Rondina et al., 2014) (We refer the reader to the recent review (Mwangi et al., 2014) for a more complete list.).”