A Multi-Modal Approach Using Cross-Modal Attention for Identifying Schizophrenia with Strong Positive Symptoms

Core Concepts

A multi-modal approach using audio, video, and text data with self and cross-modal attention mechanisms can effectively distinguish subjects with strong positive symptoms of schizophrenia from healthy controls.

Abstract

This study focuses on developing a multi-modal system to identify subjects with strong positive symptoms of schizophrenia. The system utilizes audio, video, and text data as input modalities. For the audio modality, vocal tract variables and voicing information were extracted as low-level features, which were then used to compute high-level coordination features. For the video modality, facial action units were extracted and used to compute coordination features. The text modality used context-independent word embeddings extracted from speech transcripts. The multi-modal system was developed by fusing a segment-to-session-level classifier for the audio and video modalities with a Hierarchical Attention Network (HAN) for the text modality. Self-attention and cross-modal attention mechanisms were incorporated to leverage the relationships between the different modalities. The proposed multi-modal system outperformed the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score. An ablation study was also conducted to validate the contribution of the attention mechanisms used in the multi-modal architecture. The key findings of this work are: Segment-to-session-level classifiers show better performance in video and audio modalities compared to previous approaches. The multi-modal system with self and cross-modal attention mechanisms outperforms the uni-modal and previous multi-modal systems. The text modality, despite having the lowest individual performance, contributes to the improved performance of the multi-modal system. The coherence of the text transcripts is a potential factor in the misclassifications made by the text-based model.

Stats

"Schizophrenia affects around 24 million people worldwide." "The database contains a total of 19.43 hours of 50 unique interview sessions belonging to 18 selected subjects (7 schizophrenia subjects, and 11 healthy controls)."

Quotes

"Attention mechanisms [8], [9] capture relationships between hidden states to characterize which aspects of a state's representation contribute more toward the final prediction." "Previous studies have shown promising results in identifying the severity of mental health disorders like major depressive disorder and schizophrenia using the correlation structure of the movements of various articulators [6], [12]."

Key Insights Distilled From

A multi-modal approach for identifying schizophrenia using cross-modal attention

by Gowtham Prem... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2309.15136.pdf

Deeper Inquiries

How can the multi-modal system be further improved to handle the misclassifications observed in the text-based model?

To address the misclassifications observed in the text-based model within the multi-modal system, several strategies can be implemented: Data Augmentation: Increasing the amount of text data by augmenting the existing transcripts or collecting additional data can help improve the model's performance. This can provide a more comprehensive understanding of the language patterns associated with schizophrenia symptoms. Semantic Coherence Analysis: Since semantic coherence plays a crucial role in differentiating schizophrenia subjects from healthy controls, incorporating advanced natural language processing techniques to analyze the coherence of text responses can enhance the model's ability to detect subtle linguistic cues indicative of schizophrenia. Contextual Information: Integrating contextual information from the interview setting, such as non-verbal cues or interviewer prompts, can provide a richer context for the text analysis. This contextual information can help the model better interpret the short utterances and improve the overall classification accuracy. Ensemble Learning: Employing ensemble learning techniques by combining the predictions of multiple models, including different text processing approaches or pre-trained language models, can help mitigate the weaknesses of individual models and enhance the overall classification performance. Fine-tuning Attention Mechanisms: Fine-tuning the attention mechanisms within the multi-modal system specifically for the text modality can help prioritize relevant textual features and improve the model's ability to capture the nuances of language associated with schizophrenia symptoms.

How can the insights gained from the attention mechanisms be leveraged to develop explainable AI systems that can provide clinicians with interpretable information about the key factors contributing to the classification decisions?

The insights gained from the attention mechanisms can be leveraged to develop explainable AI systems in the following ways: Attention Visualization: By visualizing the attention weights learned by the model, clinicians can gain insights into which features or modalities are most influential in the classification decisions. This visual feedback can help clinicians understand the reasoning behind the model's predictions. Feature Importance Analysis: Analyzing the attention weights can provide information on the importance of different features or modalities in the classification process. Clinicians can use this information to identify key factors contributing to the classification decisions and validate the relevance of these factors in clinical practice. Interpretable Explanations: Utilizing the attention mechanisms to generate interpretable explanations for the classification decisions can enhance the transparency of the AI system. Clinicians can access these explanations to understand the model's reasoning and trust the diagnostic outcomes. Clinical Decision Support: Integrating the attention-based insights into clinical decision support systems can assist clinicians in interpreting the model's predictions and making informed decisions about patient care. The attention mechanisms can highlight critical factors influencing the classification, aiding clinicians in diagnosis and treatment planning. Feedback Mechanism: Establishing a feedback loop where clinicians can provide input on the model's attention mechanisms can further refine the system's interpretability. Clinician feedback can help improve the relevance and accuracy of the attention weights, leading to more reliable and clinically relevant classification decisions.