The deep learning technique has been shown to be effective in addressing several image analysis tasks within the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large amounts of data with sufficient diversity in terms of image style and quality. In particular, the diversity of image styles may be primarily attributed to the vendor factor. However, the collection of mammograms from large and diverse vendors is very expensive and sometimes impractical. Motivatedly, a novel contrastive learning method is developed to equip the deep learning models with better generalization capability. Specifically, the multi-style and multi-view unsupervised self-learning scheme is carried out to seek robust feature embedding against various vendor styles as a pre-trained model. Afterward, the pre-trained 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 extensively and rigorously evaluated with mammograms from various vendor-style domains and several public datasets. The experimental results suggest that the proposed domain generalization method can effectively improve the performance of four mammographic image tasks on data from either seen or unseen domains and outperform many state-of-the-art (SOTA) generalization methods.

翻译：深度学习技术已被证明在乳腺钼靶计算机辅助诊断方案的多种图像分析任务中具有有效性。训练高效的深度学习模型需要大量兼具图像风格与质量多样性的数据。其中，图像风格的多样性主要源于设备厂商差异。然而，跨厂商大规模采集乳腺钼靶影像成本高昂且在实际中难以实现。受此启发，本文提出了一种新颖的对比学习方法，旨在提升深度学习模型的泛化能力。具体而言，通过多风格多视图无监督自学习方案，挖掘鲁棒的特征嵌入以应对不同厂商设备风格，并以此作为预训练模型。随后将预训练网络针对下游任务进行微调，包括肿块检测、匹配、BI-RADS分级及乳腺密度分类。本文使用来自多个厂商风格域及多个公开数据集的乳腺钼靶影像，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个乳腺钼靶图像任务在已知与未知域数据上的性能，并优于多种最先进的（SOTA）泛化方法。