Full Field Digital Mammograms (FFDMs) and Digital Breast Tomosynthesis (DBT) are the two most widely used imaging modalities for breast cancer screening. Although DBT has increased cancer detection compared to FFDM, its widespread adoption in clinical practice has been slowed by increased interpretation times and a perceived decrease in the conspicuity of specific lesion types. Specifically, the non-inferiority of DBT for microcalcifications remains under debate. Due to concerns about the decrease in visual acuity, combined DBT-FFDM acquisitions remain popular, leading to overall increased exam times and radiation dosage. Enabling DBT to provide diagnostic information present in both FFDM and DBT would reduce reliance on FFDM, resulting in a reduction in both quantities. We propose a machine learning methodology that learns high-level representations leveraging the complementary diagnostic signal from both DBT and FFDM. Experiments on a large-scale data set validate our claims and show that our representations enable more accurate breast lesion detection than any DBT- or FFDM-based model.

翻译：全视野数字乳腺X线摄影（FFDM）与数字乳腺断层合成（DBT）是乳腺癌筛查中应用最广泛的两种成像模态。尽管与FFDM相比，DBT提高了癌症检出率，但其在临床实践中的广泛应用因解读时间增加以及特定病灶类型显著性感知下降而受到阻碍。具体而言，DBT对微钙化的非劣效性仍存争议。出于对视觉清晰度下降的担忧，DBT-FFDM联合采集模式依然普遍，这导致整体检查时间与辐射剂量增加。若能使DBT同时提供FFDM与DBT所包含的诊断信息，将减少对FFDM的依赖，从而降低这两项指标。我们提出一种机器学习方法，该方法通过利用DBT与FFDM的互补性诊断信号来学习高层表征。在大规模数据集上的实验验证了我们的主张，并表明我们的表征能够实现比任何基于DBT或FFDM的模型更准确的乳腺病灶检测。