  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.
