Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal
• Venue: arXiv: cs.CV
• Published: 2026-06-24
• Collected: 2026-06-25
• URL: http://arxiv.org/abs/2606.25606v1
• PDF: https://arxiv.org/pdf/2606.25606v1
• DOI: 10.1109/ACII59096.2023.10388143

Plain-Language Brief

The researchers have developed a new method for diagnosing depression using deep learning models and facial videos. Their approach provides interpretable explanations for the model's decisions, which can help health care professionals understand its reasoning. This method outperforms previous single-face benchmarks in visual depression diagnosis.

What the Document Says

• The study focuses on developing a framework for analyzing deep learning models' decisions when trained on facial videos for automatic depression severity diagnosis.
• They fine-tune Deep Convolutional Neural Networks (DCNN) pre-trained on Action Recognition datasets on depression severity facial videos from AVEC dataset.
• Their approach can interpret the model's saliency maps by examining face regions and temporal expression semantics, generating both visual and quantitative explanations for the model's decisions.
• This method improves upon previous single-face benchmarks for visual depression diagnosis.

Key Concepts Mentioned

• depression diagnosis
• deep learning models
• facial videos
• interpretability
• Deep Convolutional Neural Networks (DCNN)
• AVEC dataset

Claims Made by the Document

• Their method provides interpretable explanations for the model's decisions.
• The video-based modeling has improved upon previous single-face benchmarks.

Evidence Used

• By fine-tuning DCNN on depression severity facial videos...
• Our approach generates both visual and quantitative explanations...

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care. Even though current automated depression diagnosis approaches improved over the last decade, a critical gap exists as they often lack affect-specificity and interpretability, limiting their practical application and potential impact on mental health care. In particular, interpretability from temporal activities from videos when deep models are used is not fully explored. In this study, we present a novel framework for analyzing Deep Neural Networks' decisions when trained on facial videos, specifically focusing on automatic depression severity diagnosis. By fine-tuning Deep Convolutional Neural Networks (DCNN) pre-trained on Action Recognition datasets on depression severity facial videos from AVEC depression dataset, our framework is able to interpret the model's saliency maps by examining face regions and temporal expression semantics. Our approach generates both visual and quantitative explanations for the model's decisions, providing greater insight into its reasoning. In addition to this interpretability, our video-based modeling has improved upon previous single-face benchmarks for visual depression diagnosis, resulting in enhanced predictive performance. Overall, our work demonstrates the successful development of a framework capable of generating hypotheses from a facial model's decisions while simultaneously improving depression's predictive capabilities.