基于图神经网络的流引导定位针对个体血流与活动的定制化调整
Tailoring Graph Neural Network-based Flow-guided Localization to Individual Bloodstreams and Activities
AI总结：
针对现有基于GNN的流引导定位方法无法适应个体血流差异和活动变化的问题，提出基于身高、体重和心率等生理指标的适配流水线，以调和个体差异，实现持续监测。
摘要：
利用血液中体内纳米设备进行流引导定位，有望促进早期疾病检测、生物状况的持续监测以及靶向治疗。纳米设备面临尺寸和功率限制，这些限制会产生用于定位目的的错误的原始数据。佩戴在身体上的锚节点接收这些数据，并利用它们来推导感兴趣的诊断事件的位置。近年来，针对这一任务提出了不同的机器学习（ML）方法，然而它们目前仅限于静息患者的参考血流。因此，它们无法应对患者血流的物理多样性，并且由于个体患者活动的变化而无法提供持续监测。为了解决当前基于图神经网络（GNNs）的最先进（SotA）流引导定位方法中的这些问题，我们提出了一种基于个体生理指标（包括身高、体重和心率）的GNN适配流水线。我们的结果表明，所提出的适配有助于调和血流与活动之间的个体差异。
英文摘要：
Flow-guided localization using in-body nanodevices in the bloodstream is expected to be beneficial for early disease detection, continuous monitoring of biological conditions, and targeted treatment. The nanodevices face size and power constraints that produce erroneous raw data for localization purposes. On-body anchors receive this data, and use it to derive the locations of diagnostic events of interest. Different Machine Learning (ML) approaches have been recently proposed for this task, yet they are currently restricted to a reference bloodstream of a resting patient. As such, they are unable to deal with the physical diversity of patients' bloodstreams and cannot provide continuous monitoring due to changes in individual patient's activities. Toward addressing these issues for the current State-of-the-Art (SotA) flow-guided localization approach based on Graph Neural Networks (GNNs), we propose a pipeline for GNN adaptation based on individual physiological indicators including height, weight, and heart rate. Our results indicate that the proposed adaptions are beneficial in reconciling the individual differences between bloodstreams and activities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
