Papers Depression Detection
“Depression Detection” 태그가 달린 논문 157편 · 필터 해제
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)
With the increasing prevalence and deployment of Emotion AI-powered facial affect analysis (FAA) tools, concerns about the trustworthiness of these systems have become more prominent. This first workshop on "Towards Trus…
Action Unit DetectionDepression DetectionEthicsFacial Action Unit Detection+3Towards Machine Unlearning for Paralinguistic Speech Processing
In this work, we pioneer the study of Machine Unlearning (MU) for Paralinguistic Speech Processing (PSP). We focus on two key PSP tasks: Speech Emotion Recognition (SER) and Depression Detection (DD). To this end, we pro…
Depression DetectionEmotion RecognitionMachine UnlearningSpeech Emotion RecognitionExplainable Depression Detection using Masked Hard Instance Mining
This paper addresses the critical need for improved explainability in text-based depression detection. While offering predictive outcomes, current solutions often overlook the understanding of model predictions which can…
Depression DetectionLarge Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge
Depression is a growing concern gaining attention in both public discourse and AI research. While deep neural networks (DNNs) have been used for recognition, they still lack real-world effectiveness. Large language model…
Depression DetectionDiagnosticSpeech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction
Speech is a noninvasive digital phenotype that can offer valuable insights into mental health conditions, but it is often treated as a single modality. In contrast, we propose the treatment of patient speech data as a tr…
Depression DetectionLanguage ModelingLanguage ModellingLarge Language Model+1The First MPDD Challenge: Multimodal Personality-aware Depression Detection
Depression is a widespread mental health issue affecting diverse age groups, with notable prevalence among college students and the elderly. However, existing datasets and detection methods primarily focus on young adult…
Depression DetectionEnhancing Depression Detection via Question-wise Modality Fusion
Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported qu…
Depression DetectionOrdinal ClassificationWhy Pre-trained Models Fail: Feature Entanglement in Multi-modal Depression Detection
Depression remains a pressing global mental health issue, driving considerable research into AI-driven detection approaches. While pre-trained models, particularly speech self-supervised models (SSL Models), have been ap…
Data AugmentationDepression DetectionCross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method
Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/norma…
Binary ClassificationDepression DetectionEEGElectroencephalogram (EEG)Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation
Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce professionals highlights the need for automated detection. Current system…
Depression DetectionHallucinationRetrievalRetrieval-augmented GenerationSpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information
Large Language Models (LLMs) have been increasingly adopted for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input. While Retrieval-Augmented Generation …
Depression DetectionRAGRetrievalRetrieval-augmented Generation+1Machine Learning Fairness for Depression Detection using EEG Data
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using different deep learning architectures such as …
Depression DetectionEEGElectroencephalogram (EEG)FairnessMultimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence
This study proposes an innovative multimodal fusion model based on a teacher-student architecture to enhance the accuracy of depression classification. Our designed model addresses the limitations of traditional methods …
Depression DetectionEmotion RecognitionTransfer LearningTowards Explainable Multimodal Depression Recognition for Clinical Interviews
Recently, multimodal depression recognition for clinical interviews (MDRC) has recently attracted considerable attention. Existing MDRC studies mainly focus on improving task performance and have achieved significant dev…
Decision MakingDepression DetectionExplainable artificial intelligenceMedical Diagnosis+3U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection
Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work inve…
Depression DetectionFairnessContext-Aware Deep Learning for Multi Modal Depression Detection
In this study, we focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML). Our approach differentiates from other successful ML methods such as context-aware an…
Data AugmentationDeep LearningDepression DetectionFeature EngineeringEmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
Sentiment and emotion understanding are essential to applications such as human-computer interaction and depression detection. While Multimodal Large Language Models (MLLMs) demonstrate robust general capabilities, they …
Depression DetectionEmotion-Cause Pair ExtractionEmotion RecognitionFacial Expression Recognition+3Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share …
Depression DetectionEmotion RecognitionSentiment AnalysisSynthetic Data Generation with LLM for Improved Depression Prediction
Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcit…
Depression DetectionPrivacy PreservingSentiment AnalysisSynthetic Data GenerationSAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor
Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods co…
Depression DetectionDiagnosticEEGFunctional Connectivity