Filters








986 Hits in 10.9 sec

Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data

Jorge Samper-González, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen, Anne Bertrand, Hugo Bertin (+4 others)
2018 NeuroImage  
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]

Jorge Samper-González, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen, Anne Bertrand, Hugo Bertin (+4 others)
2018 bioRxiv   pre-print
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]

Yelu Gao, Huang Huang, Lian Zhang
2022 arXiv   pre-print
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]

Junhao Wen, Jorge Samper-Gonzalez, Simona Bottani, Alexandre Routier, Ninon Burgos, Thomas Jacquemont, Sabrina Fontanella, Stanley Durrleman, Stephane Epelbaum, Anne Bertrand, Olivier Colliot
2020 arXiv   pre-print
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

Adrià Casamitjana, Paula Petrone, Alan Tucholka, Carles Falcon, Stavros Skouras, José Luis Molinuevo, Verónica Vilaplana, Juan Domingo Gispert
2018 Journal of Alzheimer's Disease  
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]

Mayssa Soussia, Islem Rekik
2018 arXiv   pre-print
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

Taeho Jo, Kwangsik Nho, Andrew J. Saykin
2019 Frontiers in Aging Neuroscience  
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

Ignacio Illán-Gala, Jordi Pegueroles, Victor Montal, Eduard Vilaplana, María Carmona-Iragui, Daniel Alcolea, Bradford C. Dickerson, Raquel Sánchez-Valle, Mony J. de Leon, Rafael Blesa, Alberto Lleó, Juan Fortea
2018 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
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  
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

James Zou, David Park, Aubrey Johnson, Xinyang Feng, Michelle Pardo, Jeanelle France, Zeljko Tomljanovic, Adam M. Brickman, Devangere P. Devanand, José A. Luchsinger, William C. Kreisl, Frank A. Provenzano (+1 others)
2021 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
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  
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

Mehdi Rahim, Bertrand Thirion, Claude Comtat, Gael Varoquaux
2016 IEEE Journal on Selected Topics in Signal Processing  
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

Deevyankar Agarwal, Gonçalo Marques, Isabel de la Torre-Díez, Manuel A. Franco Martin, Begoña García Zapiraín, Francisco Martín Rodríguez
2021 Sensors  
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]

Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
2013 arXiv   pre-print
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

Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
2014 IEEE journal of biomedical and health informatics  
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
« Previous Showing results 1 — 15 out of 986 results