Abstract
Mammography stands as the main screening method for detecting breast cancer early, enhancing treatment success rates. The segmentation of landmark structures in mammography images can aid the medical assessment in the evaluation of cancer risk and the image acquisition adequacy. We introduce a series of data-centric strategies aimed at enriching the training data for deep learning-based segmentation of landmark structures. Our approach involves augmenting the training samples through annotation-guided image intensity manipulation and style transfer to achieve better generalization than standard training procedures. These augmentations are applied in a balanced manner to ensure the model learns to process a diverse range of images generated by different vendor equipments while retaining its efficacy on the original data. We present extensive numerical and visual results that demonstrate the superior generalization capabilities of our methods when compared to the standard training. For this evaluation, we consider a large dataset that includes mammography images generated by different vendor equipments. Further, we present complementary results that show both the strengths and limitations of our methods across various scenarios. The accuracy and robustness demonstrated in the experiments suggest that our method is well-suited for integration into clinical practice.
Abstract (translated)
乳房X光摄影是早期检测乳腺癌的主要筛查方法，可以提高治疗成功率。在乳房X光图像中对标志性结构进行分割有助于医疗评估，在癌症风险评估和影像获取充分性方面发挥重要作用。我们提出了一系列以数据为中心的策略，旨在丰富基于深度学习的标志性结构分割模型的训练数据。我们的方法通过指导注释引导下的图像强度调整和风格转换来扩充训练样本，从而实现比标准训练程序更好的泛化能力。这些增强措施是以平衡的方式应用的，确保模型能够处理由不同供应商设备生成的不同类型的图像，同时在原始数据上保持其有效性。我们展示了大量的数值和视觉结果，证明了我们的方法与标准训练相比具有更优越的泛化能力。为此评估，我们考虑了一个包含来自不同供应商设备产生的乳房X光摄影图像的大规模数据集。此外，还呈现了补充结果，展示我们在各种场景下所提方法的优点和局限性。实验中表现出的准确性和鲁棒性表明我们的方法非常适合在临床实践中应用。
URL
https://arxiv.org/abs/2503.22052
