Manifold Learning of Brain MRIs by Deep Learning

By Tom Brosch, Roger C. Tam. 2013

239 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

A survey on deep learning in medical image analysis (2017, background): “Brosch and Tam (2013) DBN AD/HC classification; deep belief networks with convolutional RBMs for manifold learning”
Deep Learning for Health Informatics (2017, background): “[64] described a manifold learning approach of 3-D brain images based on DBN.”
Spectral Graph Convolutions for Population-based Disease Prediction (2017, methodology): “Such models provide powerful tools for population analysis and integration of non-imaging data such as manifold learning [2,16] or clustering algorithms [12].”
Learning Implicit Brain MRI Manifolds with Deep Learning (2018, background): “Recently, deep learning approaches like deep autoencoders and convolutional neural networks have provided a new mechanism for learning a manifold representation of data [6, 7].”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, background): “learned a low-dimensional manifold of brain volumes by the deep belief networks (DBN) algorithm to detect the modes of variations correlating to demographic and disease parameters for AD [36].”
Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease (2014, background): “Brosch and Tam, using MR, reported that the multilayered learning structure was effective in capturing shape variations of the brain regions that correlate with demographic and disease information, such as the ventricle size [38].”
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018, methodology): “manifold learning (Wolz et al., 2012; Brosch and Tam, 2013) and clustering (Parisot et al.”
Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification (2014, methodology): “Convolutional Restricted Boltzmann Machines (CRBM ) [10] were used for manifold learning by reducing the dimensionality of 3D brain magnetic resonance (MR) images [11].”
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, background): “[73] developed a deep belief network model and used manifold learning for AD detection from MRI images.”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, background): “After the success of computer vision, the ﬁrst clinical uses of deep learning were in image processing, especially the analysis of brain MRI scans to predict Alzheimer’s disease and its variations [80,81].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “DL and RL architectures have been widely used in analyzing medical images obtained from– magnetic resonance ([f/s]MRI), CT scan, positron emission tomography (PET), radiography/ fundus (e.g., X-ray, CFI), microscope, ultrasound (UlS)– to denoise, segment, classify, detect anomalies and diseases from”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, unknown): “Despite accumulating evidence showing the superiority of the DNN, previous applications of DNN to neuroimaging data are limited to only a few studies (Brosch and Tam, 2013; Hjelm et al., 2014; Plis et al., 2014; Suk et al., 2013).”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, methodology): “…Boltzmann Machines applied to Lung CT analysis combining generative as well as discriminative learning techniques [170], Deep Belief Networks trained on three dimensional medical images [171] etc. Recently, the the trend of using Convolutional Neural Networks in the ﬁeld of image processing Fig.”
Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images (2015, unknown): “Recently we showed the potential of convDBNs for discriminating be-tween Alzheimer’s and healthy subjects using their 3D MRI scans (Brosch & Tam, 2013).”
Large-Scale Unsupervised Deep Representation Learning for Brain Structure (2018, methodology): “Brosch et al. [1] proposed a method for manifold learning of brain images in the ADNI dataset using Deep Belief Networks (DBNs) composed of convolutional Restricted Boltzmann Machines.”