标题
Robust hybrid deep learning models for Alzheimer’s progression detection
作者
关键词
Computer-aided diagnosis, Information fusion, Multimodal multitask learning, Alzheimer’s disease, Alzheimer’s progression, Cognitive scores regression
出版物
KNOWLEDGE-BASED SYSTEMS
Volume 213, Issue -, Pages 106688
出版商
Elsevier BV
发表日期
2020-12-25
DOI
10.1016/j.knosys.2020.106688
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods
- (2020) Gokce Uysal et al. JOURNAL OF NEUROSCIENCE METHODS
- Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning
- (2020) Dan Pan et al. Frontiers in Neuroscience
- Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease
- (2020) Anees Abrol et al. JOURNAL OF NEUROSCIENCE METHODS
- Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer's disease
- (2020) Carlos Platero et al. JOURNAL OF NEUROSCIENCE METHODS
- Gaussian discriminative component analysis for early detection of Alzheimer’s disease: A supervised dimensionality reduction algorithm
- (2020) Chen Fang et al. JOURNAL OF NEUROSCIENCE METHODS
- Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data
- (2020) Shaker El-Sappagh et al. NEUROCOMPUTING
- Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data
- (2020) Shaker El-Sappagh et al. Future Generation Computer Systems-The International Journal of eScience
- Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling
- (2019) Mostafa Mehdipour Ghazi et al. MEDICAL IMAGE ANALYSIS
- A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
- (2019) Simeon Spasov et al. NEUROIMAGE
- Predicting Alzheimer’s disease progression using multi-modal deep learning approach
- (2019) Garam Lee et al. Scientific Reports
- Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
- (2019) P. J. Moore et al. PLoS One
- RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease
- (2019) Ruoxuan Cui et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures
- (2019) Nikhil Bhagwat et al. JOURNAL OF PSYCHIATRY & NEUROSCIENCE
- A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual
- (2019) Magda Bucholc et al. EXPERT SYSTEMS WITH APPLICATIONS
- SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data
- (2019) Yi-Fan Zhang et al. IEEE Internet of Things Journal
- A distributed multitask multimodal approach for the prediction of Alzheimer’s disease in a longitudinal study
- (2019) Solale Tabarestani et al. NEUROIMAGE
- Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease
- (2019) D. Chitradevi et al. APPLIED SOFT COMPUTING
- A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging
- (2019) Parisa Forouzannezhad et al. JOURNAL OF NEUROSCIENCE METHODS
- A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease
- (2019) Manhua Liu et al. NEUROIMAGE
- A new machine learning method for identifying Alzheimer's disease
- (2019) Lin Liu et al. SIMULATION MODELLING PRACTICE AND THEORY
- Speech emotion recognition based on DNN-decision tree SVM model
- (2019) Linhui Sun et al. SPEECH COMMUNICATION
- Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning
- (2019) Kanghan Oh et al. Scientific Reports
- A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data
- (2018) Kan Li et al. Alzheimers & Dementia
- Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging
- (2018) Hongyoon Choi et al. BEHAVIOURAL BRAIN RESEARCH
- Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge
- (2018) Nicola Amoroso et al. JOURNAL OF NEUROSCIENCE METHODS
- Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease
- (2018) Donghuan Lu et al. MEDICAL IMAGE ANALYSIS
- A hybrid computational approach for efficient Alzheimer’s disease classification based on heterogeneous data
- (2018) Xuemei Ding et al. Scientific Reports
- Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis
- (2018) Mingxia Liu et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- iForest: Interpreting Random Forests via Visual Analytics
- (2018) Xun Zhao et al. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
- RuleMatrix: Visualizing and Understanding Classifiers with Rules
- (2018) IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
- A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network
- (2018) Chujie Tian et al. Energies
- Multi-task deep convolutional neural network for cancer diagnosis
- (2018) Qing Liao et al. NEUROCOMPUTING
- Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment
- (2018) Weiming Lin et al. Frontiers in Neuroscience
- Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks
- (2018) Silvia Basaia et al. NeuroImage-Clinical
- Advancing Alzheimer’s research: A review of big data promises
- (2017) Rui Zhang et al. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
- Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease
- (2017) Liqiang Nie et al. IEEE Transactions on Neural Networks and Learning Systems
- 2016 Alzheimer's disease facts and figures
- (2016) Alzheimers & Dementia
- On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey
- (2016) Ane Alberdi et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Enhancing Fingrams to deal with precise fuzzy systems
- (2016) David P. Pancho et al. FUZZY SETS AND SYSTEMS
- Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method
- (2016) Mikel Elkano et al. INFORMATION SCIENCES
- Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest
- (2016) Lei Huang et al. NEUROBIOLOGY OF AGING
- Instantiated mixed effects modeling of Alzheimer's disease markers
- (2016) R. Guerrero et al. NEUROIMAGE
- Domain Transfer Learning for MCI Conversion Prediction
- (2015) Bo Cheng et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer's disease: a prospective cohort study
- (2015) Wai-Ying Wendy Yau et al. LANCET NEUROLOGY
- Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
- (2015) Elaheh Moradi et al. NEUROIMAGE
- A Robust Deep Model for Improved Classification of AD/MCI Patients
- (2015) Feng Li et al. IEEE Journal of Biomedical and Health Informatics
- Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data
- (2014) Jesus J. Gomar et al. Alzheimers & Dementia
- Multi-objective evolutionary algorithms for fuzzy classification in survival prediction
- (2014) Fernando Jiménez et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Modeling disease progression via multi-task learning
- (2013) Jiayu Zhou et al. NEUROIMAGE
- Joint Modeling of Transitional Patterns of Alzheimer's Disease
- (2013) Wei Liu et al. PLoS One
- Multiple Kernel Learning in the Primal for Multimodal Alzheimer’s Disease Classification
- (2013) Fayao Liu et al. IEEE Journal of Biomedical and Health Informatics
- Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
- (2012) Chong-Yaw Wee et al. HUMAN BRAIN MAPPING
- A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort
- (2012) Bruno M. Jedynak et al. NEUROIMAGE
- Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data
- (2011) Youngsang Cho et al. NEUROIMAGE
- Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
- (2011) Daoqiang Zhang et al. NEUROIMAGE
- Derivation of a New ADAS-cog Composite Using Tree-based Multivariate Analysis
- (2010) Daniel A. Llano et al. ALZHEIMER DISEASE & ASSOCIATED DISORDERS
- Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database
- (2010) Kaori Ito et al. Alzheimers & Dementia
- The clinical use of structural MRI in Alzheimer disease
- (2010) Giovanni B. Frisoni et al. Nature Reviews Neurology
- Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance
- (2010) Michael Ewers et al. NEUROBIOLOGY OF AGING
- High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables
- (2010) Ying Wang et al. NEUROIMAGE
- Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database
- (2010) Rémi Cuingnet et al. NEUROIMAGE
- Relating one-year cognitive change in mild cognitive impairment to baseline MRI features
- (2009) Simon Duchesne et al. NEUROIMAGE
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