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:乳腺摄影图像分析是计算机辅助诊断方案中的基本问题,随着深度学习的发展取得了显着进展。然而,构建深度学习模型需要大量且具有足够多样性的图像训练数据,尤其是图像样式的多样性可能主要归因于供应商因素。然而,对尽可能多的供应商进行乳腺X线摄影检查的收集非常昂贵,有时对实验室规模的研究来说是不切实际的。因此,为了进一步增强深度学习模型对具有有限资源的各种供应商的泛化能力,开发了一种新的对比学习方案。具体来说,首先使用多样式和多视图无监督自学习方案训练骨干网络,以得到对各个供应商样式不变的特征嵌入。随后,将骨干网络重新校准到乳腺X线摄影检查的下游任务中,包括肿块检测,多视图肿块匹配,BI-RADS分类和乳腺密度分类,并使用特定的监督学习方法。提出的方法使用来自四个供应商和两个不可见公共数据集的乳腺X线摄影图像进行评估。实验结果表明,我们的方法可以有效地提高对已知和未知领域的分析性能,并优于许多最先进的泛化方法。