Breast cancer is one of the main causes behind cancer deaths in women worldwide. Yet, owing to the complexity of the histopathological images and the arduousness of manual analysis task, the entire diagnosis process becomes time consuming and the results are often contingent on pathologist's subjectivity. Thus developing an automated, precise histopathological image classification system is crucial. This paper presents a novel hybrid ensemble framework consisting of multiple fine-tuned convolutional neural network (CNN) architectures as supervised feature extractors and eXtreme gradient boosting trees (XGBoost) as a top level classifier, for patch wise classification of high resolution breast histopathology images. Due to semantic complexity of the patch images, a single CNN architecture may not always extract high quality features, and the traditional Softmax classifier might not provide ideal results for classifying the CNN extracted features. Thus we aim to improve patch wise classification by proposing a hybrid ensemble model which incorporates different discriminating feature representations of the patches, coupled with XGBoost for robust classification. Experimental results show that our proposed method outperforms state-of-the art methods to the best of our knowledge.

中文翻译：

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使用深度卷积特征和梯度增强树分类器的混合集合，从高分辨率乳腺显微图像中进行癌类型分类。

乳腺癌是全世界女性死于癌症的主要原因之一。然而，由于组织病理学图像的复杂性和手动分析任务的艰巨性，整个诊断过程变得很耗时，并且结果通常取决于病理学家的主观性。因此，开发自动化，精确的组织病理学图像分类系统至关重要。本文提出了一种新颖的混合集成框架，该框架由多个微调卷积神经网络（CNN）架构（作为监督特征提取器）和eXtreme梯度增强树（XGBoost）作为顶级分类器组成，用于高分辨率乳腺组织病理学图像的分片明智分类。由于补丁图片的语义复杂性，单个CNN架构可能无法始终提取高质量的特征，传统的Softmax分类器可能无法为分类CNN提取的特征提供理想的结果。因此，我们旨在通过提出一种混合集成模型来改进逐块分类，该模型集成了补丁的不同区分特征表示，并结合了XGBoost进行稳健的分类。实验结果表明，据我们所知，我们提出的方法优于最新方法。