Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease

Cheng, Bo; Liu, Mingxia; Shen, Dinggang; Li, Zuoyong; Zhang, Daoqiang

doi:10.1007/s12021-016-9318-5

Cited by 74 publications

(39 citation statements)

References 64 publications

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“…Wachinger et al (2016) proposed an instance re-weighting framework to improve the accuracy of Alzheimer's Disease (AD) diagnosis by making the source domain data to have similar distributions as target domain data. Cheng et al (2012Cheng et al ( , 2015aCheng et al ( ,b, 2017 proposed several workflows to perform domain adaptation to improve AD diagnosis accuracy by leveraging data of mild cognitive impairment, which is considered as the early stage of AD.…”

Section: Introductionmentioning

confidence: 99%

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

Zhou

Cox

2019

Machine Learning in Medical Imaging

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Section: Introductionmentioning

confidence: 99%

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

Zhou

Cox

2019

Machine Learning in Medical Imaging

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“…3) Utilizing feature selection, either prior to the classifier design or jointly with the classifier design as a regularizer (e.g. Tohka et al (2016); Michel et al (2011); Cheng et al (2017)).…”

Section: Introductionmentioning

confidence: 99%

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging

Sevilla-Salcedo

Gómez-Verdejo

Tohka

et al. 2019

Preprint

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A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description.We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer's disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30 − 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods.

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“…In the recent years, there were published a vast number of papers dedicated to classification of healthy controls from AD using deep learning approach applied to neuroimaging. However, only a few works considered predicting conversion of MCI to AD ( [5], [6], [8]), which is a more complicated and clinically relevant problem. To classify stable and converged MCI the authors of [6] used different clinical biomarkers and complex feature maps extracted from neuroimaging.…”

Section: Introductionmentioning

confidence: 99%

Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

Shmulev

Belyaev

2018

Lecture Notes in Computer Science

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Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimers Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem as approximately 15% of patients with MCI converges to AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is ACC = 0.76±0.01 and the area under the ROC curve -ROC AUC = 0.86 ± 0.01.

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Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease

Cited by 74 publications

References 64 publications

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging

Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

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