Personalized Diagnosis for Alzheimer’s Disease
Current learning-based methods for the diagnosis of Alzheimer’s Disease (AD) rely on training a general classifier aiming to recognize abnormal structural alternations from homogenously distributed dataset deriving from a large population. However, due to diverse disease pathology, the real imaging data in routine clinic practices is highly complex and heterogeneous. Hence, prototype methods commonly performing well in the laboratory cannot achieve expected outcome when applied under the real clinic setting. To address this issue, herein we propose a novel personalized model for AD diagnosis. We customize a subject-specific AD classifier for the new testing data by iteratively reweighting the training data to reveal the latent testing data distribution and refining the classifier based on the weighted training data. Furthermore, to improve estimation of diagnosis result and clinical scores at the individual level, we extend our personalized AD diagnosis model to a joint classification and regression scenario. Our model shows improved performance on classification and regression accuracy when applied on Magnetic Resonance Imaging (MRI) selected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our work pinpoints the clinical potential of personalized diagnosis framework in AD.
- 2.Thompson, P.M., Hayashi, K.M., Dutton, R.A., Chiang, M.-C., Leow, A.D., Sowell, E.R., et al.: Tracking Alzheimer’s disease. In: Annals of New York Academy of Sciences, vol. 1097, pp. 198–214 (2007)Google Scholar
- 3.Zhu, Y., Zhu, X., Kim, M., Shen, D., Wu, G.: Early diagnosis of alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 264–272. Springer, Cham (2016). doi: 10.1007/978-3-319-46720-7_31 CrossRefGoogle Scholar
- 4.Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Zu, C., Nie, F., Shen, D., Wu, G.: Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 291–299. Springer, Cham (2016). doi: 10.1007/978-3-319-46720-7_34 CrossRefGoogle Scholar
- 7.Gretton, A., et al.: Covariate shift by kernel mean matching. In: Dataset Shift in Machine Learning, pp. 123–135 (2009)Google Scholar