Honors Thesis 2020 - Renxuan Li


Hierarchical Transformer for Early Detection of Alzheimer’s Disease

Renxuan Li

Highest Honor in Computer Science


Abstract

Alzheimer's disease is an irreversible disease that severely affect the brain functions and life quality of the patients. For now, there is no effective cure for the disease. Therefore this unfortunate fact makes the early detection of Alzheimer's disease vital. The early stage of the Alzheimer's disease, Mild Cognitive Impairment (MCI), normally involve loss in memory, language ability, and object recognition ability. In this paper, we present a new dataset that includes the transcribed audio of MCI patients and healthy subject. We also present a hierarchical transformer-based model and the corresponding analysis for the MCI/health classification task on our dataset.

Department / School

Computer Science / Emory University

Degree / Year

BS / Spring 2020

Committee

Jinho D. Choi, Emory University (Chair)
Michelangelo Grigni, Emory University
Hiram Maxim, Emory University

Links

Anthology | Paper | Presentation