Random forest-based similarity measures for multi-modal classification of Alzheimer's disease

By Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert. NeuroImage, 2013

454 citations recorded by the source corpus.

Subfields: Neurology, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/f93dd4e69d86e465ff402ba98cfb752abaee7115

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Citations in the corpus

Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, unknown): “…(Fan et al., 2008; Duchesne et al., 2008; Chupin et al., 2009; Querbes et al., 2009; Wolz et al., 2011; Hinrichs et al., 2011; Westman et al., 2011a,b; Westman et al., 2012; Cho et al., 2012; Coupé et al., 2012; Gray et al., 2013; Eskildsen et al., 2013; Guerrero et al., 2014; Wang et al., 2014).”
Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling (2018, methodology): “To further demonstrate the effectiveness of this proposed system of 8-layer convolutional neural network, it was compared with 8 existing algorithms, BRC [4], TJM [5], RF [6], DF + SVM [7], BBO [8], MVA [9], PZM+ SCG [10], and PZM+ LRC [11].”
Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review (2017, background): “Authors analyzed FDG-PET data, acquired 24 months after the ﬁrst visit and already pre-processed by ADNI.”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, unknown): “[15] K.”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “Also, Gray et al. (2013) trained random forest classifiers on regional GM volumetric measures, CSF biomarkers, voxel-based FDG-PET intensities, and APOE genotype for AD and MCI classification.”
ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, background): “, remote sensing [37], microarray data [38] and Alzheimer’s disease [39].”
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013, background): “…is that they considered only simple low-level features such as cortical thickness and/or gray matter tissue volumes from MRI (Klo¨ppel et al. 2008; Gray et al. 2013; Zhang et al. 2011; Zhang and Shen 2012; Cui et al. 2011; Desikan et al. 2009; Walhovd et al. 2010; Yao et al. 2012; Westman et…”
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge (2015, methodology): “, 2011) and MCI (Petersen, 2004) were used, which is common practice in studies of computer-aided diagnosis methods (Cuingnet et al., 2011; Klöppel et al., 2008; Falahati et al., 2014; Davatzikos et al., 2008a; Duchesne et al., 2008; Fan et al., 2008a,b; Gray et al., 2013; Koikkalainen et al., 2012;”
Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness (2014, background): “A large number of studies addressing the use of pattern recognition methods in image-based detection of AD have been published in recent years (Gray et al., 2013; Liu et al., 2012; Cuingnet et al., 2011; Klöppel et al., 2008).”