Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database

By Rémi Cuingnet, Emilie Gerardin, Jérôme Tessieras, Guillaume Auzias, Stéphane Lehéricy, Marie Odile Habert, Marie Chupin, Habib Benali, Olivier Colliot. NeuroImage, 2011

1,037 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/eeaa6012a81d2ce6eb1f3ef92fd1ef7d7418d32d

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Citations in the corpus

Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, methodology): “In recent years, the neuroimaging based computer-aided diagnosis (CAD) for AD has attracted considerable attention [4], [8]–[10].”
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge (2015, methodology): “…for AD (McKhann et al., 2011) and MCI (Petersen, 2004) were used, which is a 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…”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “…that images were spatially normalized to a common stereotaxic space (e.g. Shen and Davatzikos, 2002; Ashburner, 2007) as in many group studies or classification methods (Lao et al., 2004; Fan et al., 2007; Klöppel et al., 2008a; Vemuri et al., 2008; Querbes et al., 2009; Cuingnet et al., 2011).”
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, methodology): “A recent study with a subset of ADNI subjects assessed the classification performance of several structural MRI methods in experiments comparable to our investigation [29].”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, neutral): “However, AD vs. NC classi ﬁ cation is an easier problem than sMCI vs. pMCI classi ﬁ cation (Cuingnet et al., 2011), especially if the number of labeled training data is small (Filipovych and Davatzikos, 2011).”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, neutral): “MRI preprocessing A proper image preprocessing procedure is a fundamental step to ensure a good classification performance, especially in the domain of MRI (Cuingnet et al., 2011; Lu and Weng, 2007; Uchida, 2013).”
Generative-Discriminative Basis Learning for Medical Imaging (2012, methodology): “The values reported in the Table II for the dataset are in the same range as the accuracy rates reported in [61]; Nevertheless, the conditions of the experiments (including preprocessing, features extraction, samples in the training and testing lists, etc.”
Alzheimer's disease diagnostics by adaptation of 3D convolutional network (2016, background): “, AD, mild cognitive impairment (MCI), and normal control (NC) groups [3, 6–8].”