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光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包含多样风格和质量的大规模数据，而数据多样性通常源于不同厂商扫描仪的使用。然而在实践中，收集足量多样化数据进行训练并不现实。为此，本文开发了一种新型对比学习方法，使深度学习模型具备更优的风格泛化能力。具体而言，我们设计了多风格多视角无监督自学习方案，通过预训练模型获取对风格多样性具有鲁棒性的特征嵌入。随后，预训练网络被进一步微调至下游任务，如肿块检测、匹配、BI-RADS分级和乳腺密度分类。我们使用来自不同厂商风格域和多个公开数据集的乳腺X光影像，对所提方法进行了全面严格的评估。实验结果表明，所提出的域泛化方法能有效提升四种乳腺X光影像任务在可见域和不可见域数据上的性能，并优于多种最先进的（SOTA）泛化方法。