SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification

Yuexi Du, Regina J. Hooley, John Lewin, Nicha C. Dvornek
3/19/2024
eess.IVcs.CVcs.LG

No official code found. Best implementation: XYPB/SIFT_DBT (6 stars, MIT, last commit 2025-09-22).

Abstract

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.

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Code Implementations(1)

XYPB/SIFT_DBTCommunity100%
MIT

Official Implementation for ISBI 24 paper "SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification"

62PythonFeb 8, 20241 years agoMIT

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Cite this paper

@article{du2024siftdbt,
  title  = {SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification},
  author = {Yuexi Du and Regina J. Hooley and John Lewin and Nicha C. Dvornek},
  year   = {2024},
  eprint = {2403.13148},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.13148v1}
}

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