SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification
<|reference_start|>SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification: 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.<|reference_end|>
@article{du2024sift-dbt:,
    title={SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced
  Digital Breast Tomosynthesis Image Classification},
    author={Yuexi Du, Regina J. Hooley, John Lewin, Nicha C. Dvornek},
    journal={arXiv preprint arXiv:2403.13148},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13148},
    primaryClass={eess.IV cs.CV cs.LG}
}