We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion annotations. Our approach is implemented using an all convolutional design that is simple yet provides superior performance in comparison with the previous methods. On DDSM, our best single-model achieves a per-image AUC score of 0.88 and three-model averaging increases the score to 0.91. On INbreast, our best single-model achieves a per-image AUC score of 0.96. Using DDSM as benchmark, our models compare favorably with the current state-of-the-art. We also demonstrate that a whole image model trained on DDSM can be easily transferred to INbreast without using its lesion annotations and using only a small amount of training data. Code availability: https://github.com/lishen/end2end-all-conv

本文提出了一种基于全乳腺拍摄的乳腺癌诊断的全图像训练算法，不依赖于病变的标注，使用图像标签即可训练出全图像分类器，并在DDSM数据集上取得了0.88的AUC分数，在INbreast数据集上取得了0.96的AUC分数，并且可轻松将DDSM的全图像模型转移到INbreast。

使用全卷积设计进行乳腺癌整体图像诊断的端到端训练