In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH). As these histology images are too large to fit into GPU memory, we first propose using Inception V3 to perform patch level classification. The patch level predictions are then passed through an ensemble fusion framework involving majority voting, gradient boosting machine (GBM), and logistic regression to obtain the image level prediction. We improve the sensitivity of the Normal and Benign predicted classes by designing a Dual Path Network (DPN) to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using GBM, logistic regression, and support vector machine (SVM) to refine predictions. Experimental results demonstrate our framework shows a 12.5$\%$ improvement over the state-of-the-art model.
翻译:在这项工作中,我们作为提交国际图像分析和识别会议(ICIAR) 2018年关于Breast癌症历史学图像的重大挑战(BACH)的呈文,提出了一个关于多级乳腺癌图像分类的深层次学习框架。由于这些神学图像太大,无法与GPU记忆相适应,我们首先提议使用Inception V3来进行补丁等级分类。然后通过一个包含多数选票、梯度助推机(GBM)和物流回归的混合框架来进行补丁级预测,以获得图像水平的预测。我们设计了一个普通和班尼恩预测班的灵敏度,设计了一个双向路径网络(DPN),作为地貌提取器使用,这些提取的特征被进一步发送到第二层使用GBM、物流回归和支持矢量机(SVM)来改进预测。实验结果显示,我们的框架显示,与最先进的模型相比,我们的框架有12.5%的改善。