Automatic Depression Detection via Learning and Fusing Features from Visual Cues

• URL: http://arxiv.org/abs/2203.00304v1
• Date: Tue, 1 Mar 2022 09:28:12 GMT
• Title: Automatic Depression Detection via Learning and Fusing Features from Visual Cues
• Authors: Yanrong Guo, Chenyang Zhu, Shijie Hao, Richang Hong
• Abstract summary: We propose a novel Automatic Depression Detection (ADD) method via learning and fusing features from visual cues. Our method achieves the state-of-the-art performance on the DAIC_WOZ dataset compared to other visual-feature-based methods.
• Score: 42.71590961896457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depression is one of the most prevalent mental disorders, which seriously affects one's life. Traditional depression diagnostics commonly depends on rating with scales, which can be labor-intensive and subjective. In this context, Automatic Depression Detection (ADD) has been attracting more attention for its low cost and objectivity. ADD systems are able to detect depression automatically from some medical records, like video sequences. However, it remains challenging to effectively extract depression-specific information from long sequences, thereby hindering a satisfying accuracy. In this paper, we propose a novel ADD method via learning and fusing features from visual cues. Specifically, we firstly construct Temporal Dilated Convolutional Network (TDCN), in which multiple Dilated Convolution Blocks (DCB) are designed and stacked, to learn the long-range temporal information from sequences. Then, the Feature-Wise Attention (FWA) module is adopted to fuse different features extracted from TDCNs. The module learns to assign weights for the feature channels, aiming to better incorporate different kinds of visual features and further enhance the detection accuracy. Our method achieves the state-of-the-art performance on the DAIC_WOZ dataset compared to other visual-feature-based methods, showing its effectiveness.

Related papers

• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• A BERT-Based Summarization approach for depression detection [1.7363112470483526]
  Depression is a globally prevalent mental disorder with potentially severe repercussions if not addressed. Machine learning and artificial intelligence can autonomously detect depression indicators from diverse data sources. Our study proposes text summarization as a preprocessing technique to diminish the length and intricacies of input texts.
  arXiv  Detail & Related papers  (2024-09-13T02:14:34Z)
• STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data [12.344849949026989]
  We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating CNN and RNN to capture both temporal and spatial features. Experiments demonstrate that STANet superior depression diagnostic performance with 82.38% accuracy and a 90.72% AUC.
  arXiv  Detail & Related papers  (2024-07-31T04:06:47Z)
• Attention-Based Acoustic Feature Fusion Network for Depression Detection [11.972591489278988]
  We present the Attention-Based Acoustic Feature Fusion Network (ABAFnet) for depression detection. ABAFnet combines four different acoustic features into a comprehensive deep learning model, thereby effectively integrating and blending multi-tiered features. We present a novel weight adjustment module for late fusion that boosts performance by efficaciously synthesizing these features.
  arXiv  Detail & Related papers  (2023-08-24T00:31:51Z)
• The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection [69.88072583383085]
  This work shows that depression changes the correlation between features extracted from speech. Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
  arXiv  Detail & Related papers  (2023-07-06T09:54:35Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• Deep Temporal Modelling of Clinical Depression through Social Media Text [1.513693945164213]
  We develop a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms.
  arXiv  Detail & Related papers  (2022-10-28T18:31:52Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)
• Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning [59.249322621035056]
  We propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented. Our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied.
  arXiv  Detail & Related papers  (2020-05-02T06:41:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.