Wolfram Research, Champaign, Illinois, United States of America.
Faculty of Sciences, University of Craiova, Craiova, Romania.
PLoS One. 2019 Jan 16;14(1):e0209274. doi: 10.1371/journal.pone.0209274. eCollection 2019.
The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners.
本研究旨在通过优化预测器的组合，实现对结肠癌组织的病理切片进行自动分类，将其分为四组（健康组织和 1 级、2 级或 3 级癌症）。为了完成这一任务，我们考虑了 6 种不同的分类器，它们的预测准确率在 87%到 95%之间。所提出的组合方法考虑了每个样本被分配到四个类别中任意一个类别的个体分类器的概率，通过差分进化优化了每个技术的权重，并获得了显著优于个体结果的准确性。此外，还定义了一个置信度，这可以让病理学家将数据分为两个不同的集合，一个是具有高度置信度的正确分类，其余的则需要他们进一步关注。该串联方法还在其他基准数据集上进行了验证。所提出的方法在提高单独使用的每个算法的分类准确性方面非常有效，并且即使使用默认权重，在其他数据集上也表现良好。此外，通过建立置信度，该方法对于实际从业者来说更加可行。
