Multimodal fusion diagnosis of depression and anxiety based on face video-Reference-Cited by-同舟云学术

Multimodal fusion diagnosis of depression and anxiety based on face video

Published:2021-11-12 Issue: Volume: Page:
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Container-title:2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE)
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Author:

Wang Chen¹,Liang Lizhong²,Liu Xiaofeng³,Lu Yao²,Shen Jihong¹,Luo Hui²,Xie Wanqing¹

Affiliation:

1. College of the Mathematical Sciences, Harbin Engineering University,Harbin,China

2. School of Computer Science and Engineering, Sun Yat-sen University,Guangzhou,China

3. Neurology, Beth Israel Deaconess Medical Center. Harvard Medical School,Boston,MA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9698819/9698829/09698881.pdf?arnumber=9698881

Reference25 articles.

1. Self-Rating Depression Scale in an Outpatient Clinic

2. Psychometric Attributes of the Self-Rating Anxiety Scale

3. Beyond short snippets: Deep networks for video classification;ng;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015

4. Measuring depression symptom severity from spoken language and 3D facial expressions;haque;ArXiv Preprint,2018

5. ImageNet classification with deep convolutional neural networks

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1. SFF-DA: Spatiotemporal Feature Fusion for Nonintrusively Detecting Anxiety;IEEE Transactions on Instrumentation and Measurement;2024

2. LI-FPN: Depression and Anxiety Detection from Learning and Imitation;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

3. Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model;Computerized Medical Imaging and Graphics;2022-12