Mammographic image analysis is a fundamental problem in the computer-aided diagnosis scheme, which has recently made remarkable progress with the advance of deep learning. However, the construction of a deep learning model requires training data that are large and sufficiently diverse in terms of image style and quality. In particular, the diversity of image style may be majorly attributed to the vendor factor. However, mammogram collection from vendors as many as possible is very expensive and sometimes impractical for laboratory-scale studies. Accordingly, to further augment the generalization capability of deep learning models to various vendors with limited resources, a new contrastive learning scheme is developed. Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor styles. Afterward, the backbone network is then recalibrated to the downstream tasks of mass detection, multi-view mass matching, BI-RADS classification and breast density classification with specific supervised learning. The proposed method is evaluated with mammograms from four vendors and two unseen public datasets. The experimental results suggest that our approach can effectively improve analysis performance on both seen and unseen domains, and outperforms many state-of-the-art (SOTA) generalization methods.

翻译：乳腺X线影像分析是计算机辅助诊断方案中的基础问题，近年来随着深度学习的发展取得了显著进展。然而，深度学习模型的构建需要大量且图像风格与质量足够多样的训练数据。其中，图像风格的多样性主要受设备厂商因素影响，但尽可能多地收集来自不同厂商的乳腺X光片成本高昂，有时在实验室规模研究中并不现实。为此，为在有限资源下进一步提升深度学习模型对不同厂商数据的泛化能力，本文提出了一种新的对比学习方案。具体而言，首先采用多风格多视角无监督自学习策略训练骨干网络，以嵌入对各类厂商风格具有不变性的特征；随后，通过特定监督学习将该骨干网络重新调整至肿块检测、多视角肿块匹配、BI-RADS分类及乳腺密度分类等下游任务。该方法使用来自四个厂商的乳腺X光片及两个未见公开数据集进行评估。实验结果表明，本方法能有效提升在可见与不可见域上的分析性能，并优于多种现有最优（SOTA）泛化方法。