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Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data
2018
NeuroImage
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We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. ...
FDG PET outperformed T1 MRI for all classification tasks. ...
The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California ...
doi:10.1016/j.neuroimage.2018.08.042
pmid:30130647
fatcat:hclzgslfxzgqnbhsrps5knaeu4
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
[article]
2018
bioRxiv
pre-print
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We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. ...
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). ...
We then demonstrated the use of the framework on different classification tasks based on T1 MRI and FDG PET data. ...
doi:10.1101/274324
fatcat:uggk6jvvfbflnfmzxb7ocjb7gm
Predicting Alzheimer's Disease Using 3DMgNet
[article]
2022
arXiv
pre-print
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In this paper, we propose a novel 3DMgNet architecture which is a unified framework of multigrid and convolutional neural network to diagnose Alzheimer's disease (AD). ...
For the diagnosis of Alzheimer's disease, a series of scales are often needed to evaluate the diagnosis clinically, which not only increases the workload of doctors, but also makes the results of diagnosis ...
Introduction Alzheimer's disease (AD) is the most common form of geriatric cognitive disorder and accounts for 60% to 70% of cases of dementia. ...
arXiv:2201.04370v1
fatcat:nl47f5dxdnderghbj7sl6yzh4q
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
[article]
2020
arXiv
pre-print
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In the present paper, we first extend a previously proposed framework to diffusion MRI data for AD classification. ...
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. ...
We recently proposed a framework for the reproducible evaluation of machine learning algorithms in AD classification and demonstrated its use on PET and T1w MRI data (Samper-González et al. 2018 ). ...
arXiv:1812.11183v4
fatcat:2364nwolunay5ibfpixfaotccq
MRI-Based Screening of Preclinical Alzheimer's Disease for Prevention Clinical Trials
2018
Journal of Alzheimer's Disease
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Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment when used in a simulated ...
In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. ...
Indeed, the benefit of this method relies on adding value to MRI images that have already been acquired, prior to PET scans in secondary prevention trials. ...
doi:10.3233/jad-180299
pmid:30010132
fatcat:5x76ggug3fgtbhzfuqajvctqmu
A Review on Image- and Network-based Brain Data Analysis Techniques for Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis
[article]
2018
arXiv
pre-print
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In this study, we reviewed neuroimaging-based technical methods developed for AD and mild-cognitive impairment (MCI) classification and prediction tasks, selected by screening all MICCAI proceedings published ...
Unveiling pathological brain changes associated with Alzheimer's disease (AD) is a challenging task especially that people do not show symptoms of dementia until it is late. ...
, reproducibility, and generalizability to unseen data [25] . ...
arXiv:1808.01951v1
fatcat:zj2p6w5xw5abbdfhhs6ubfzz74
Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data
2019
Frontiers in Aging Neuroscience
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The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has ...
Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. ...
In the case of diagnostic classification for the progressive and irreversible Alzheimer's disease, all subsequent MRI images should be labeled as belonging to a patient with Alzheimer's disease. ...
doi:10.3389/fnagi.2019.00220
pmid:31481890
pmcid:PMC6710444
fatcat:udknjrow3rf5fkr7bkjcswy3jy
Challenges associated with biomarker-based classification systems for Alzheimer's disease
2018
Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
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We aimed to evaluate the consistency of the A/T/N classification system. ...
-G. and J.P. contributed equally to this work. 1 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc .edu). ...
In this study, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) multimodal biomarker data to evaluate for the first time: (i) the consistency of available biomarkers for subject classification ...
doi:10.1016/j.dadm.2018.03.004
pmid:30175226
pmcid:PMC6114028
fatcat:vd7osmndk5bdpo4dzrpt2oqtye
A Non Invasive Biomarkers for Alzheimer's disease Detection
2019
International journal of recent technology and engineering
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Alzheimer's disease (AD) is one of the most common neurodegenerative diseases occurring in elderly population worldwide, which usually starts slowly and worsens over time. ...
Objective of this survey is to review the non-invasive biomarkers that could be used to predict early onset of AD and delay cognitive impairment. ...
Based on MR and PET ADNI data repository, proposed framework [23] outperformed the state-of-the art SVM-based method and other deep learning frameworks. ...
doi:10.35940/ijrte.b1001.0782s519
fatcat:af6kyeyxknalvj2vkwxtbwg3oe
Deep learning improves utility of tau PET in the study of Alzheimer's disease
2021
Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
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or qualitative evaluation techniques in earlier disease states. ...
CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands. ...
Indeed, our prior work has demonstrated the value of deep learning in pure MRI, both in AD and stable and progressing MCI, 14 as a standalone method and supplement to existing biomarkers. ...
doi:10.1002/dad2.12264
pmid:35005197
pmcid:PMC8719427
fatcat:lovusrvg3fasxee62mtzffcfma
Applying deep learning models on structural MRI for stage prediction of Alzheimer's disease
2019
Turkish Journal of Electrical Engineering and Computer Sciences
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In this study, neuroimaging biomarkers were applied for the diagnosis of Alzheimer's disease and dementia as a noninvasive method. ...
Alzheimer's disease is a brain disease that causes impaired cognitive abilities in memory, concentration, planning, and speaking. ...
In order to obtain a large amount of patient samples, images were reproduced by applying some data augmentation methods. ...
doi:10.3906/elk-1904-172
fatcat:atalfzuszbbvnak3rdjzrnfcnu
Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction
2016
IEEE Journal on Selected Topics in Signal Processing
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France. § Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ...
As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ...
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense ...
doi:10.1109/jstsp.2016.2600400
pmid:28496560
pmcid:PMC5421559
fatcat:nqzczk5w7zeu5knns5zzdhdhgm
Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review
2021
Sensors
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Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. ...
However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to ...
Competitions such as Alzheimer's disease Big Data Challenge [11] and the challenge of predicting MCI from MRI data [12] have revealed usefulness in the AD detection process by providing a common platform ...
doi:10.3390/s21217259
pmid:34770565
pmcid:PMC8587338
fatcat:pb5katsykffirodccpejeieibm
Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification
[article]
2013
arXiv
pre-print
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To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. ...
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. ...
It has found successful applications in genomic data fusion [20] , protein function prediction [21] etc. As for AD data fusion and classification, Hinrichs et al. ...
arXiv:1310.0890v1
fatcat:z6l2gd3nc5gabmwicviqie4z4e
Multiple Kernel Learning in the Primal for Multimodal Alzheimer's Disease Classification
2014
IEEE journal of biomedical and health informatics
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To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. ...
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. ...
It has found successful applications in genomic data fusion [20] , protein function prediction [21] etc. As for AD data fusion and classification, Hinrichs et al. ...
doi:10.1109/jbhi.2013.2285378
pmid:24132030
fatcat:er2i773g2vfzpoeirs4vojozma
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