DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 6, 2025

Abstract

BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast cancer screening, wherein a DL-based model is commonly developed in an institute and then tested in other institutes. One challenge of SSDG is to alleviate the domain shifts using only one domain dataset.

Authors

  • Wanfang Xie
    School of Engineering Medicine, Beihang University, Beijing, 100191, China.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Litao Zhao
    School of Engineering Medicine, Beihang University, Beijing, 100191, China.
  • Meiyun Wang
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Jiangang Liu
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.

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