Hierarchical Transformer for Early Detection of Alzheimer’s Disease 公开
Li, Renxuan (Spring 2020)
Published
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 the 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
Table of Contents
Contents
1 Introduction .................1
2 Related Works .................4
2.1 DetectionofAlzheimer’sDisease................. 4
2.2 Transformers for Natural Language Understanding . . . . . . 6
3 Dataset..............................9
3.1 B-SHARP: Brain, Stress, Hypertension, and Aging Research Program.............................. 9
3.2 SpeechTaskProtocol....................... 11
3.3 ComparisontoDementiaBank.................. 13
4 Approaches 16
4.1 BaselinesApproaches....................... 16
4.1.1 Convolutional Neural Network(CNN) . . . . . . . . . . 16
4.1.2 CNN-LSTM........................ 16
4.2 HierarchicalTransformer..................... 17
5 Experiments 21
5.1 DataSplit............................. 21
5.2 Transformers ........................... 22
5.3 PerformanceonIndividualTasks ................ 23
5.4 PerformancewithEnsembleApproaches . . . . . . . . . . . . 24
6 Analysis 26
6.1 AttentionAnalysis ........................ 26
6.1.1 MethodsandMeasures.................. 26
6.1.2 RoBERTaAnalysis.................... 28
6.1.3 BERT Analysis and Comparison to RoBERTa . . . . . 36
6.1.4 Findingvalidation .................... 40
6.2 EnsembleAnalysis ........................ 45
6.2.1 RoBERTaEnsembleAnalysis .............. 45
7 Conclusion 53
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