Adversarially Robust Feature Learning for Breast Cancer Diagnosis

arXiv:2402.08768v1 Announce Type: cross
Abstract: 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 …

abstract adversarial application applications arxiv cancer cancer diagnosis clean data cs.lg data deep learning diagnosis eess.iv feature novel robust standard study type world