AutoEncoder-based Feature Ranking for Predicting Mild Cognitive Impairment Conversion using FDG-PET Images-Reference-Cited by-同舟云学术

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AutoEncoder-based Feature Ranking for Predicting Mild Cognitive Impairment Conversion using FDG-PET Images

Published:2023-07-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Statistical Signal Processing Workshop (SSP)
language:
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Author:

Tuan Pham Minh¹,Trung Nguyen Linh²,Adel Mouloud¹,Guedj Eric¹

Affiliation:

1. Aix-Marseille Université,Marseille,France

2. University of Engineering and Technology, Vietnam National University,Hanoi,Vietnam

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10207925/10207926/10208072.pdf?arnumber=10208072

Reference16 articles.

1. Feature Selection

2. Autoencoders, minimum description length and Helmholtz free energy;hinton;Advances in neural information processing systems,1994

3. Effectiveness and Safety of 18F-FDG PET in the Evaluation of Dementia: A Review of the Recent Literature

4. Support-vector networks

5. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas

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