Early diagnosis of Alzheimer's disease with deep learning

By Siqi Liu, Sidong Liu, Weidong (Tom) Cai, Sonia Pujol, Ron Kikinis, D. Feng. 2014

489 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Psychiatry and Mental health

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, methodology): “…and using Bayes rule to estimate the posterior, P ð D jE ; θ Þ ¼ P ðEj D ; θ Þ P ð D ; θ Þ P ðE ; θ Þ : Examples of discriminative diagnostic models include neural network and deep learning models 8,10,15,33,34 , whereas generative models are typically Bayesian networks 18,19,21,22,27,35,36 .”
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment (2017, methodology): “While the application of ANNs to medical image analysis is well established for some clinical applications, its use for neurological applications has only lately become more popular [51, 50, 34, 33, 9, 49, 48, 22].”
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks (2015, result): “[10] report on a deep fully-connected network pre-trained with stacked autoencoders which is then ﬁne-tuned; however, their approach does not use convolution operations, contrary to ours.”
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries (2019, background): “Deep learning (DL) is widely used in many domains, including aircraft collision avoidance systems [1], Alzheimer’s disease diagnosis [2], autonomous driving cars [3], and romance storytelling [4], [5].”
Deep Learning in Medical Imaging: General Overview (2017, background): “Few studies have incorporated deep learning based approaches for AD diagnosis and in these systems, wherein the diseases can be assessed from multi-modal brain data due to the effective features generated from deep learning (89-91).”
Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications (2017, unknown): “Notably, there is also preliminary evidence for the use of DL to distinguish between individuals at no imminent risk of dementia, those identified at risk who will and will not develop dementia, and those with established Alzheimer‟s disease (Liu et al., 2015a; Liu et al., 2014; Suk et al., 2015b).”
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks (2019, background): “Siqi Liu et al. [38] also achieved a classification accuracy of approximately 85.53% with the same network architecture for 2D CNNs. Sarraf et al. [14] performed research for classification of AD.”
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].”
Preserving differential privacy in convolutional deep belief networks (2017, background): “2010), Alzheimer’s diagnosis (Liu et al. 2014; Ortiz et al. 2016), risk prediction for chronic kidney disease progression (Perotte et al.”
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems (2019, background): “Brain images have been extensively studied to diagnose Alzheimers disease (Liu et al., 2014) and tumor segmentation (Menze et al.”
Deep learning for healthcare: review, opportunities and challenges (2018, unknown): “Following the success in computer vision, the first applications of deep learning to clinical data were on image processing, especially on the analysis of brain Magnetic Resonance Imaging (MRI) scans to predict Alzheimer disease and its variations [51, 52].”
Residual and plain convolutional neural networks for 3D brain MRI classification (2017, background): “Recently, there appeared several publications that use deep learning for 3D structural magnetic resonance images (MRI) processing, with particular algorithms ranging from manifold learning with Restricted Boltzmann Machines [1] to a combination of autoencoders and classifiers [2] and to meta analysi”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “In recent years, a large amount of work has been published on DL-based prediction tasks, such as brain age prediction [136]–[138], Alzheimer’s disease classification and trajectory modeling [139]–[142], and schizophrenia classification [143], [144].”