Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%).

该研究比较了一种手工特征提取器和五种基于深度学习的迁移学习特征提取器，发现在乳腺癌数字病理图像分类中，预训练于ImageNet数据集上的深度学习网络比传统的手工特征提取方法表现更好，并通过随机森林算法将不同特征组合起来，将平均准确率提高至最高93.00%。

利用基于差异性多视角学习的方法，提高乳腺癌组织学图像的迁移学习性能