The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various scanners of vendors. But, in practice, it is impractical to collect a sufficient amount of diverse data for training. To this end, a novel contrastive learning is developed to equip the deep learning models with better style generalization capability. Specifically, the multi-style and multi-view unsupervised self-learning scheme is carried out to seek robust feature embedding against style diversity as a pretrained model. Afterward, the pretrained network is further fine-tuned to the downstream tasks, e.g., mass detection, matching, BI-RADS rating, and breast density classification. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets. The experimental results suggest that the proposed domain generalization method can effectively improve performance of four mammographic image tasks on the data from both seen and unseen domains, and outperform many state-of-the-art (SOTA) generalization methods.

翻译：深度学习技术已被证明能有效解决乳腺X线计算机辅助诊断方案中的多项图像分析任务。训练高效的深度学习模型需要具有多样风格和质量的大规模数据，数据多样性通常源于不同厂商扫描仪的使用。然而实际中难以收集足量多样化数据用于训练。为此，本文开发了一种新型对比学习方法来赋予深度学习模型更优的风格泛化能力。具体而言，我们通过多风格多视图无监督自学习方案，在预训练阶段寻求对风格多样性具有鲁棒性的特征嵌入。随后对预训练网络进行微调，使其适配肿块检测、匹配、BI-RADS分级及乳腺密度分类等下游任务。本方法已在多个厂商风格域及公共数据集上经过广泛严格的评估，实验结果表明，所提域泛化方法能有效提升四种乳腺X线图像任务在可见与不可见域数据上的性能，并优于多项现有最优泛化方法。