Adversarially Robust Feature Learning for Breast Cancer Diagnosis
Degan Hao Dooman Arefan Margarita Zuley Wendie Berg Shandong Wu
Adversarial data can lead to malfunction of deep learning applications. It is
essential to develop deep learning models that are robust to adversarial data
while accurate on standard, clean data. In this study, we proposed a novel
adversarially robust feature learning (ARFL) method for a real-world
application of breast cancer diagnosis. ARFL facilitates adversarial training
using both standard data and adversarial data, where a feature correlation
measure is incorporated as an objective function to encourage learning of
robust features and restrain spurious features. To show the effects of ARFL in
breast cancer diagnosis, we built and evaluated diagnosis models using two
independent clinically collected breast imaging datasets, comprising a total of
9,548 mammogram images. We performed extensive experiments showing that our
method outperformed several state-of-the-art methods and that our method can
enhance safer breast cancer diagnosis against adversarial attacks in clinical
settings.