通过空间分布异常模式，提前发现肺部CT中新型传染病的早期迹象。
Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns
- 基于三维CNN中间层的格拉姆矩阵，捕捉病变的空间分布特征。
- 对比新旧患者群体的空间模式，发现累积证据可识别疾病爆发起点。
- 适用于突发传染病监测，尤其对表现熟悉但分布异常的新病种有效。
快速检测新兴传染病对遏制传播和有效治疗至关重要。局部异常虽相关，但新病常表现为已知疾病模式在新空间分布中的出现，传统局部异常检测方法可能无法识别。本文提出一种新方法，通过分析肺部CT中病灶的空间分布模式，检测新疾病表型的出现。首先识别肺部CT中的异常，再将新患者队列的分布模式与长期历史数据进行对比。利用持续积累的患者数据流中的证据，评估其对新兴疾病爆发的检测能力。在三维卷积神经网络中间层提取的格拉姆矩阵表示中，新出现的聚类即指示新兴疾病。该方法能有效捕捉非典型空间分布的新型传染病。
原文摘要 · Abstract (English)
Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to detect the emergence of new disease phenotypes exhibiting distinct patterns of the spatial distribution of lesions. We first identify anomalies in lung CT data, and then compare their distribution in a continually acquired new patient cohorts with historic patient population observed over a long prior period. We evaluate how accumulated evidence collected in the stream of patients is able to detect the onset of an emerging disease. In a gram-matrix based representation derived from the intermediate layers of a three-dimensional convolutional neural network, newly emerging clusters indicate emerging diseases.
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