While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis, features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer ...

中文翻译：

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DCT-MIL：深度CNN转移了多实例学习，以使用CT图像进行COPD识别。

尽管已使用许多预定义的计算机断层扫描（CT）措施来表征慢性阻塞性肺疾病（COPD），但代表多维和高度空间异质性的病理变化仍然是挑战。提出了深度CNN转移多实例学习（DCT-MIL）以通过CT图像识别COPD的方法。将肺沿轴向分为八个部分后，从每个部分中取出一个随机的轴向CT图像作为一个实例。以一个实例为输入，提取自然图像训练的AlexNet神经层的激活作为特征。通过主成分分析进行降维后，所有实例的特征都输入了三种MIL方法：引用k最近邻（Citation-KNN），多实例支持向量机，和期望最大化的不同密度。此外，结果模型对神经层深度的性能依赖性...