Domain Transfer Learning for MCI Conversion Prediction

Cheng, Bo; Zhang, Daoqiang; Shen, Dinggang

doi:10.1007/978-3-642-33415-3_11

Cited by 39 publications

(6 citation statements)

References 13 publications

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“…As shown in many studies [14], [20], [23], [28], the disease information of AD and NC subjects are helpful for separating PMCI and SMCI. The hypothesis justifying this is that the subjects with SMCI are more NC-like while subjects who go on to develop dementia are more AD-like.…”

Section: Calculation Of Global Grading Biomakermentioning

confidence: 99%

“…Since the population of MCI subjects is highly heterogeneous, previous studies [14], [20], [23], [26], [27], [28] have shown that the inclusion of AD and normal controls (NC) subjects can be beneficial for the classification between SMCI and PMCI. A semi-supervised learning method [20], [27] was used to integrate information from AD and NC subject to augment the prediction of MCI-to-AD conversion.…”

Section: Introductionmentioning

confidence: 99%

“…A semi-supervised learning method [20], [27] was used to integrate information from AD and NC subject to augment the prediction of MCI-to-AD conversion. Auxiliary data from AD and NC subjects were used in a domain transfer learning method [28] to aid the classification of MCI subjects. In [14], classifiers learned for separating AD and NC subjects were directly used to distinguish between SMCI and PMCI.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Tong

Gao

Ledig

et al. 2017

IEEE Trans. Biomed. Eng.

141

144

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Abstract-Objective: Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance (MR) images.Methods: We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion.Results: Using the ADNI dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79%-81% for the prediction of MCI-to-AD conversion within 3 years in 10-fold cross validations. The classification AUC further increases to 84%-92% when age and cognitive measures are combined with the proposed grading biomarker.Conclusion: The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space; the removal of the normal aging effect; selection of discriminative voxels; the calculation of the grading biomarker using AD and normal control groups; the integration of sparse representation technique and the combination of cognitive measures.Significance: The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.

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Section: Calculation Of Global Grading Biomakermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Tong

Gao

Ledig

et al. 2017

IEEE Trans. Biomed. Eng.

141

144

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show abstract

“…Finally, we can note that even though our method is based on one imaging modality it performs similarly or even better than recent multi-modality methods [6,18,19]. Furthermore, for clinical reasons, it could be interesting to analyze [8] 71 70 71 Multi-instance learning [9] 70.4 66.5 73.1 Multi-methods [4] 68 67 69 Cortical thickness [7] 67.8 64.6 70.0 the anatomical regions selected via SLR.…”

Section: Resultsmentioning

confidence: 87%

Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease

Komlagan

Ta²,

Pan

et al. 2014

Lecture Notes in Computer Science

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Abstract. The early detection of Alzheimer's disease (AD) is a key step to accelerate the development of new therapies and to diminish the associated socio-economic burden. To address this challenging problem, several biomarkers based on MRI have been proposed. Although numerous efforts have been devoted to improve MRI-based feature quality or to increase machine learning methods accuracy, the current AD prognosis accuracy remains limited. In this paper, we propose to combine both high quality biomarkers and advanced learning method. Our approach is based on a robust ensemble learning strategy using gray matter grading. The estimated weak classifiers are then fused into high informative anatomical sub-ensembles. Through a sparse logistic regression, the most relevant anatomical sub-ensembles are selected, weighted and used as input to a global classifier. Validation on the full ADNI1 dataset demonstrates that the proposed method obtains competitive results of prediction of conversion to AD in the Mild Cognitive Impairment group with an accuracy of 75.6%.

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“…Si et al propose a framework for transfer subspace learning via dimensionality reduction [20]. In [21], Cheng et al use domain transfer SVM in MCI conversion prediction.…”

Section: Introductionmentioning

confidence: 99%

Quadratic divergence regularized SVM for optic disc segmentation

Cheng¹,

Tao²,

Wong³

et al. 2017

Biomed. Opt. Express

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Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.

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Domain Transfer Learning for MCI Conversion Prediction

Cited by 39 publications

References 13 publications

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease

Quadratic divergence regularized SVM for optic disc segmentation

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