PICU面部和胸腔腹部检测使用自主监督的分割时空mamba
Mohamed Khalil Ben Salah1, Philippe Jouvet2, Rita Noumeir1
1Biomedical Information Processing Laboratory, École de Technologie Supérieure, University of Quebec, Montreal, QC H3C 1K3, Canada.
Life (Basel, Switzerland)
|November 27, 2025
概括
我们开发了分割时空马巴,用于儿童重症监护室的非接触式生命体征监测. 这种高效的架构确保了稳定的解剖跟踪和可靠的生理信号提取,即使数据有限.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在儿科重症监护室 (PICU) 进行非接触式生命体征监测,面临着闭塞和数据稀缺等挑战.
- 现有的方法在稳定的解剖学跟踪方面扎,以获得可靠的生理信号提取.
- 传统的探测器缺乏稳定性,视频变压器对于临床硬件来说是计算密集型的.
研究的目的:
- 引入一个高效和强大的架构,用于PICU中非接触式生命体征监测.
- 解决现有方法在跟踪稳定性,数据稀缺性和计算成本方面的局限性.
- 为了从患者视频中实现实时,可靠的生理信号提取.
主要方法:
- 开发了使用状态空间模型解空间和时间特征学习的分割时空Mamba (一种新型架构).
- 在超过5万个特定域名的视频片段上,使用面具自动编码器实施了自我监督的预训,以解决数据稀缺问题.
- 使用多式联网RGB-D输入来增强模型的稳定性.
主要成果:
- 实现了线性时间复杂度,比标准变压器低92%.
- 通过0.96 mAP@0.5,0.62 mAP50-95和0.95旋转IOU表现出卓越的性能.
- 在23 FPS和43毫秒的延迟下运行,在速度上超过VideoMAE和YOLOv8.
结论:
- 分隔时空马巴为PICU中非接触式生命体征监测提供了一个计算效率高和强大的解决方案.
- 拟议的方法克服了数据稀缺性,并确保稳定的解剖追踪可靠的生理信号提取.
- 该模型的实时性能使其适合在资源有限的环境中进行临床部署.
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