相关实验视频

Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

FBANet:使用功能增强的双层注意力网络转移学习以识别抑郁症

Huayi Wang¹, Jie Zhang¹, Yaocheng Huang¹

¹Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.

Entropy (Basel, Switzerland)

|September 28, 2023

概括

一个新的深度学习模型,FBANet,准确地从House-Tree-Person (HTP) 草图中识别抑郁症. 这种自动化方法为心理健康评估提供了更客观,更有效的方法.

科学领域:

心理学心理学心理学
计算机科学计算机科学
人工智能的人工智能

背景情况:

房子-树-人 (HTP) 草图测试是评估心理健康的心理工具.
目前从HTP草图中识别抑郁症通常依赖于主观的手动分析,限制了自动化和客观性.
使用机器学习/深度学习的现有自动化方法具有复杂的管道,阻碍了实际应用.

研究的目的:

开发一种高度自动化,准确和高效的基于深度学习的方法,用于从HTP草图中识别抑郁症.
为改善抑郁症检测引入一种新的一级深度学习架构.

主要方法:

设计了一个新的功能增强双层注意力网络 (FBANet),结合了功能增强和双层注意力模块.
使用转移学习,预先在大型草图数据集上训练模型,然后在手绘的HTP草图数据集上微调它.
交叉验证用于对HTP数据集进行可靠的性能评估.

主要成果:

在HTP草图数据集上,FBANet的最大准确率为99.07%,平均准确率为97.71%.
与传统分类模型和先前的工作相比,提出的单阶段方法显示出更高的性能.
该模型展示了一个简单的数据预处理管道和计算过程,表明了高度的自动化.

结论:

在预训练后,FBANet模型在HTP草图中的抑郁症识别方面表现出色.
这种深度学习方法为抑郁症的辅助诊断提供了一个有希望的,自动化的,准确的工具.
FBANet解决了心理健康评估的传统HTP草图分析中主观性和低自动化的局限性.

关键词:

两个级别的注意力注意力.通过交叉验证验证.抑郁症的识别抑郁症的识别功能增强功能增强功能增强转移学习转移学习

更多相关视频

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

相关概念视频

Depression: Overview

Depression: Overview

Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...

Long-term Depression

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.

Depressive Disorders: Etiology

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...