Junbo Ma

TL;DR: A novel personalized diagnosis technique is proposed for early Alzheimer’s disease diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture and outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis.

TL;DR: Hu et al. as discussed by the authors proposed a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multiband functional connectivity biomarkers from functional neuroimaging data.

TL;DR: Wang et al. as discussed by the authors proposed a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the intersubject variability, and the heterogeneity across subjects.

TL;DR: This chapter briefly overviews various deep learning models and their learning principles, and demonstrates the hands-on experience of developing deep neural networks for medical image analysis with several typical applications such as feature representation learning, image segmentation, and image registration.

TL;DR: A novel Attention-Guided Deep Graph Neural (AGDGN) network is proposed in this paper, which utilizes an Attention- Guided Random Walk (AGRW) module to extract the structural graph features from the brain network and the global attention mechanism is integrated into the sequence processing module.