End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer’s disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.
- 1.Adeli, E., Kwon, D., Pohl, K.M.: Multi-label transduction for identification of disease comorbidity patterns. In: MICCAI (2018)Google Scholar
- 2.Association, Alzheimer’s: 2017 Alzheimer’s Disease Facts and Figures. Alzheimers Dement 13, 325–373 (2017)Google Scholar
- 3.Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 126–130. IEEE (2016)Google Scholar
- 4.Jack, C.R., et al.: The alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Resonance Imaging 27(4), 685–691 (2008)Google Scholar
- 9.Liu, M., Zhang, J., Adeli, E., Shen, D.: Deep multi-task multi-channel learning for joint classification and regression of brain status. In: MICCAI (2017)Google Scholar
- 11.Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. (2002)Google Scholar