Attention-guided erasing for enhanced transfer learning in breast abnormality classification.

Journal: International journal of computer assisted radiology and surgery
PMID:

Abstract

PURPOSE: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.

Authors

  • Adarsh Bhandary Panambur
    Siemens Healthineers, Karl Heinz Kaske Str. 5, 91052, Erlangen, Bayern, Germany. adarsh.bhandary.panambur@fau.de.
  • Sheethal Bhat
    Siemens Healthineers, Karl Heinz Kaske Str. 5, 91052, Erlangen, Bayern, Germany.
  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
  • Prathmesh Madhu
    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Siming Bayer
    Siemens Healthineers, Karl Heinz Kaske Str. 5, 91052, Erlangen, Bayern, Germany.
  • Andreas Maier
    Pattern Recognition Lab, University Erlangen-Nürnberg, Erlangen, Germany.