Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compare the performance of eight state-of-the-art detection methods, including Transformer-based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline is designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalizes better than state-of-the-art transfer learning-based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis is performed to identify the covariate shifts with bigger effects on the detection performance, such as due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning-based breast cancer detection.
翻译:在这项工作中,我们探索了在数字乳房X射线摄影中大规模检测的深层学习方法的广域化,深入分析了大规模多中心环境中的广域转移源。为此,我们比较了八个最先进的检测方法的性能,包括以变异器为基础的模型,这些模型在单一领域受过培训并在五个隐蔽领域接受测试。此外,设计了一个单一源大规模检测培训管道是为了改进域的全域化,而不需要新领域的图像。结果显示,我们的工作流程比在五个领域中的四个领域采用最先进的转移学习方法要好,同时减少了由不同的采购协议和扫描仪制造商造成的区域转移。随后,我们进行了广泛的分析,以查明对检测性能有更大影响的共变式变化,例如由于病人年龄、乳房密度、质量大小和大规模恶性肿瘤的差异。最终,这项全面研究为未来深度癌症研究提供了关键的全域洞察和最佳实践。