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 the 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 the 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 that 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成从高分辨率乳腺显微镜图像中进行癌症类型分类


乳腺癌是全世界女性癌症死亡的主要原因之一。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，整个诊断过程变得非常耗时，并且结果往往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，由作为监督特征提取器的多个微调卷积神经网络（CNN）架构和作为顶级分类器的极限梯度提升树（XGBoost）组成，用于高分辨率乳腺组织病理学的补丁式分类图像。由于补丁图像的语义复杂性，单个 CNN 架构可能无法始终提取高质量的特征，并且传统的 Softmax 分类器可能无法为 CNN 提取的特征进行分类提供理想的结果。因此，我们的目标是通过提出一种混合集成模型来改进补丁分类，该模型结合了补丁的不同判别特征表示，并与 XGBoost 相结合以进行鲁棒分类。实验结果表明，据我们所知，我们提出的方法优于最先进的方法。