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, Computer Science and QTM, Emory University (Chair)
Michelangelo Grigni, Computer Science, Emory University
Hiram Maxim, German Studies, Emory University

Links

Anthology | Paper | Presentation