在检测精神障碍背景下探索自监督学习的任务无关特性
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
AI总结：
本研究在检测MDD和PTSD背景下，利用音频和视频数据探索SSL生成的任务无关表示，通过提出固定目标列表和修改SSL编码器超参数生成全局表示，提升了精神障碍检测性能并验证了任务无关特性。
摘要：
自监督学习（SSL）已被研究用于在各个领域生成任务无关的表示。然而，尚未有此类研究针对检测多种精神障碍进行。任务无关表示存在的原理在于多种精神障碍之间存在重叠的症状。因此，为心理健康评估收集的行为数据可能包含与多种障碍相关的混合属性。受此启发，本研究在检测重度抑郁症（MDD）和创伤后应激障碍（PTSD）的背景下，利用交互式会话中收集的音频和视频数据，探索通过SSL推导的任务无关表示。本研究采用通过预测多个固定目标或掩码帧训练的SSL模型。我们提出了一系列固定目标，使生成的表示在检测MDD和PTSD时更高效。此外，我们修改了预测固定目标的SSL编码器的超参数，以生成捕获不同时间上下文的全局表示。这两项创新均被注意到能提升所考虑精神障碍的检测性能，并展现出任务无关特性。在预测掩码帧的SSL模型背景下，生成的全局表示也被注意到展现出任务无关特性。
英文摘要：
Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among multiple mental disorders. Consequently, the behavioural data collected for mental health assessment may carry a mixed bag of attributes related to multiple disorders. Motivated by that, in this study, we explore a task-agnostic representation derived through SSL in the context of detecting major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) using audio and video data collected during interactive sessions. This study employs SSL models trained by predicting multiple fixed targets or masked frames. We propose a list of fixed targets to make the generated representation more efficient for detecting MDD and PTSD. Furthermore, we modify the hyper-parameters of the SSL encoder predicting fixed targets to generate global representations that capture varying temporal contexts. Both these innovations are noted to yield improved detection performances for considered mental disorders and exhibit task-agnostic traits. In the context of the SSL model predicting masked frames, the generated global representations are also noted to exhibit task-agnostic traits.
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