The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists’ burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-resolution images, and the need to combine information from multiple views and scales still pose technical challenges. Multi-view architectures that combine information from the four mammographic views to produce an exam-level classification score are a promising approach to the automated processing of screening mammography. However, training such architectures from exam-level labels, without relying on pixel-level supervision, requires very large datasets and may result in suboptimal accuracy. Emerging architectures such as Visual Transformers (ViT) and graph-based architectures can potentially integrate ipsi-lateral and contra-lateral breast views better than traditional convolutional neural networks, thanks to their stronger ability of modeling long-range dependencies. In this paper, we extensively evaluate novel transformer-based and graph-based architectures against state-of-the-art multi-view convolutional neural networks, trained in a weakly-supervised setting on a middle-scale dataset, both in terms of performance and interpretability. Extensive experiments on the CSAW dataset suggest that, while transformer-based architecture outperform other architectures, different inductive biases lead to complementary strengths and weaknesses, as each architecture is sensitive to different signs and mammographic features. Hence, an ensemble of different architectures should be preferred over a winner-takes-all approach to achieve more accurate and robust results. Overall, the findings highlight the potential of a wide range of multi-view architectures for breast cancer classification, even in datasets of relatively modest size, although the detection of small lesions remains challenging without pixel-wise supervision or ad-hoc networks.

中文翻译：

---

使用多视图深度学习技术进行乳腺 X 线摄影分类：研究基于图形和转换器的架构


深度学习系统在提供独立评估和减轻放射科医生筛查乳腺 X 线摄影负担方面的潜力和前景已在多项研究中得到认可。然而，癌症患病率低、需要处理高分辨率图像以及需要组合来自多个视图和尺度的信息仍然构成技术挑战。多视图架构将来自四个乳腺 X 线摄影视图的信息组合在一起，以生成检查级分类分数，是自动处理筛查乳腺 X 线摄影的一种很有前途的方法。但是，在不依赖像素级监督的情况下，从考试级标签训练此类架构需要非常大的数据集，并且可能会导致准确性不佳。视觉转换器 （ViT） 和基于图的架构等新兴架构可能比传统的卷积神经网络更好地集成同侧和对侧乳房视图，这要归功于它们对长期依赖关系进行建模的能力更强。在本文中，我们广泛评估了基于变压器和基于图的新型架构与最先进的多视图卷积神经网络，这些网络在中等规模数据集的弱监督环境中进行了训练，在性能和可解释性方面。对 CSAW 数据集的广泛实验表明，虽然基于 transformer 的架构优于其他架构，但不同的归纳偏置导致互补的优势和劣势，因为每种架构对不同的体征和乳腺 X 光特征敏感。因此，应该优先选择不同架构的集合，而不是赢家通吃的方法，以获得更准确和稳健的结果。 总体而言，这些发现强调了广泛的多视图架构在乳腺癌分类中的潜力，即使在相对适中的数据集中也是如此，尽管在没有像素监督或临时网络的情况下检测小病变仍然具有挑战性。