论文标题
使用生物标志物，体积放射线学和3D CNN的肺结节分类
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs
论文作者
论文摘要
我们提出了一种杂种算法来估计肺结核恶性肿瘤，该肺结恶性肿瘤将放射科医生的注释与CT扫描的图像分类结合在一起。我们的算法采用3D卷积神经网络（CNN）以及随机森林，以将CT图像与生物标志物注释和体积放射线特征相结合。我们仅使用图像，仅生物标志物，组合图像 +生物标志物，组合图像 +体积放射素特征，最后是图像 +生物标志物 +体积特征的组合，以分析算法的性能，以分析算法的性能。国家癌症研究所（NCI）肺图像数据库财团（LIDC）IDRI数据集用于培训和评估分类任务。 We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features are combined with descriptive biomarkers.出乎意料的是，我们还表明，仅使用图像生物标志物的模型比将生物标志物与体积放射线学，3D CNN和半监督学习结合的模型更为准确。我们讨论了该结果可能受到LIDC-IDRI认知偏差影响的可能性，因为与生物标志物相同的放射科医生小组记录了恶性估计，以及未来的工作，以在研究参与者的一部分中纳入病理信息。
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in order to classify the suspicion level of nodule malignancy. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features are combined with descriptive biomarkers. Unexpectedly, we also show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the possibility that this result may be influenced by cognitive bias in LIDC-IDRI because malignancy estimates were recorded by the same radiologist panel as biomarkers, as well as future work to incorporate pathology information over a subset of study participants.
