Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.

通过使用深度多实例网络对乳腺摄影进行分类，本文成功地实现了对乳腺癌进行计算机辅助诊断，消除了传统方法中昂贵的手动标注和特定计算模型的需要。该文在INbreast数据集上开展的实验结果表明，所提出的深层网络相对于先前使用分割和检测注释进行训练的工作具有更好的鲁棒性。

稀疏标签分配的深度多实例网络用于乳腺X线照片整体分类