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English(EN) M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification

新框架利用多模态脑成像改善跨站点重度抑郁症分类

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了M2LG-DG，一个新颖的框架，旨在利用不同临床站点之间的静息态功能磁共振成像（rs-fMRI）数据来改善重度抑郁症（MDD）的分类。这种多模态方法整合了局部和全局大脑连接模式与非成像数据，将表示分解为共享和私有组件。通过采用跨站点监督对比目标，M2LG-DG鼓励模型在不同的采集域中保留诊断信息。该框架表现强劲，在四个预留的REST-meta-MDD站点上实现了69.48%的AUC，并为其他神经影像分类任务展现了潜力。 AI

影响 该框架可以提高人工智能驱动的心理健康状况诊断工具在不同临床环境中的可靠性。

排序理由 研究论文，详细介绍了一个用于医学影像分类的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架利用多模态脑成像改善跨站点重度抑郁症分类

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研究论文，详细介绍了一个用于医学影像分类的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew ·

    M2LG-DG：一种用于跨站点重度抑郁症分类的多模态局部-全局域泛化框架

    arXiv:2609.09186v1 Announce Type: new Abstract: Classification models based on resting-state functional magnetic resonance imaging (rs-fMRI) often show lower performance at imaging sites not included during model development, which can limit their use in clinical settings. Domain…

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