Interpretable Transformers for Alzheimer Disease Diagnosis on Multi-modal Data
Author:
Affiliation:
1. University of Technology Sydney,School of Computer Sceince,Sydney,Australia
2. University of New South Wales,School of Business,Canberra,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651416.pdf?arnumber=10651416
Reference27 articles.
1. Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review
2. Alzheimer’s Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning
3. Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients
4. A Machine Learning-Based Classification Method for Monitoring Alzheimer’s Disease Using Electromagnetic Radar Data
5. Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer’s Disease Diagnosis
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