Digital Breast Tomosynthesis (DBT) is a widely used medical imaging modality
for breast cancer screening and diagnosis, offering higher spatial resolution
and greater detail through its 3D-like breast volume imaging capability.
However, the increased data volume also introduces pronounced data imbalance
challenges, where only a small fraction of the volume contains suspicious
tissue. This further exacerbates the data imbalance due to the case-level
distribution in real-world data and leads to learning a trivial classification
model that only predicts the majority class. To address this, we propose a
novel method using view-level contrastive Self-supervised Initialization and
Fine-Tuning for identifying abnormal DBT images, namely SIFT-DBT. We further
introduce a patch-level multi-instance learning method to preserve spatial
resolution. The proposed method achieves 92.69% volume-wise AUC on an
evaluation of 970 unique studies.

数字乳腺断层摄影（DBT）是一种广泛使用的医学影像模态，用于乳腺癌筛查和诊断，通过其类似于 3D 的乳腺体积成像能力提供更高的空间分辨率和更多的细节。然而，增加的数据量也引入了明显的数据不平衡挑战，其中只有一小部分体积包含可疑组织，这进一步加剧了真实世界数据中的案例级分布引起的数据不平衡，并导致学习一个只预测多数类的微不足道的分类模型。为了解决这个问题，我们提出了一种新颖的方法，使用视角级对比自我监督初始化和微调来识别异常 DBT 图像，即 SIFT-DBT。我们进一步引入了一种基于补丁级的多实例学习方法来保持空间分辨率。所提出的方法在 970 个独立研究的评估中达到了 92.69％的卷积 AUC。