基于adaboost的集成学习的病理前列腺图像自动分类

Chao-Hui Huang, E. Kalaw
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引用次数: 5

摘要

我们提出了一种基于adaboost的集成学习,用于支持前列腺癌(PRCA)的自动Gleason分级。该方法能够区分Gleason模式4-5和模式1-3,因为模式4-5与更具侵袭性的疾病相关,而模式1-3往往反映更有利的患者预后。该方法基于多种颜色通道的特征描述符和分类器,包括红色、绿色和蓝色的颜色通道,以及苏木精和伊红染色的光学强度。基于adaboost的集成学习方法将颜色通道、特征描述符和分类器集成在一起,最终构建一个强分类器。我们对癌症基因组图谱(TCGA)中获得的组织病理图像和相应的医学报告进行10倍交叉验证,准确率达到97.8%。因此,该方法可用于支持前列腺癌的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated classification for pathological prostate images using AdaBoost-based Ensemble Learning
We present an AdaBoost-based Ensemble Learning for supporting automated Gleason grading of prostate adenocarcinoma (PRCA). The method is able to differentiate Gleason patterns 4–5 from patterns 1–3 as the patterns 4–5 are correlated to more aggressive disease while patterns 1–3 tend to reflect more favorable patient outcome. This method is based on various feature descriptors and classifiers for multiple color channels, including color channels of red, green and blue, as well as the optical intensity of hematoxylin and eosin stainings. The AdaBoost-based Ensemble Learning method integrates the color channels, feature descriptors and classifiers, and finally constructs a strong classifier. We tested our method on the histopathological images and the corresponding medical reports obtained from The Cancer Genome Atlas (TCGA) using 10-fold cross validation, the accuracy achieved 97.8%. As a result, this method can be used to support the diagnosis on prostate cancer.
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