Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

  title={Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging},
  author={Jiaming Guo and Wei Qiu and Xiang Li and Xuandong Zhao and N. Guo and Quanzheng Li},
  journal={2019 IEEE International Conference on Big Data (Big Data)},
  • Jiaming Guo, Wei Qiu, +3 authors Quanzheng Li
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • 2019 IEEE International Conference on Big Data (Big Data)
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep… Expand
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