Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble.
Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble.
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DOI：
10.1007/978-3-319-66179-7_18
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发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
Saltz JH
中科院分区：
文献类型：
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作者：
Dmitriev K;Kaufman AE;Javed AA;Hruban RH;Fishman EK;Lennon AM;Saltz JH
胰腺囊肿有许多不同类型。这些囊肿的范围从完全良性到恶性，在临床实践中确定确切的囊肿类型可能具有挑战性。这项工作描述了一种自动分类算法，该算法使用计算机断层扫描图像对四种最常见的胰腺囊肿类型进行分类。所提出的方法利用了患者的一般人口统计信息以及囊肿的成像外观。它基于随机森林分类器和依赖于精细纹理信息的新卷积神经网络的贝叶斯组合，随机森林分类器学习子类特定的人口统计、强度和形状特征。使用 134 名患者的 10 倍交叉验证对所提出的方法进行定量评估，报告分类准确率为 83.6%。
There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%.