MvWsT: Muti-View Deep Network with Weakly-Supervised  Learning for Predicting Alzheimer’s Disease

The chronic neurodegenerative condition, which causes dementia and permanent cognitive loss in older persons, is known as Alzheimer’s Disease (AD). The early diagnosis of AD has recently benefited from the application of computer-aided technology. However, the diversity of AD neuroimaging and genetic data and the need for professional annotation of labels impact diagnostic performance. Taking gain of the multi-view data and relieving the problem triggered by way of the lack of labeling of part of the data, a novel Deep Learning (DL) model with <strong>M</strong>ulti-<strong>v</strong>iew learning and <strong>W</strong>eakly-<strong>s</strong>upervised learning based on a <strong>T</strong>ransformer network is proposed, called MvWsT. The existence of consistency and complementarity of this data across different views is exploited to obtain a more powerful representation containing shared features and complementary features. At the same time, weakly-supervised learning is introduced to reduce the annotation of data, taking into account the particularity of the high cost of medical data annotation. The study utilizes Magnetic Resonance Imaging (MRI) to analyze neuroimaging in relation to AD, including two views in the axial view and sagittal view, with three Transformer models as baselines. Moreover, the proposed MvWsT method is validated by setting the unlabeled proportion and orthogonality constraints to complete the weakly supervised training. The results show the proposed MvWsT model has tremendous potential compared to the baselines with the single view.