Longitudinal Mammogram Exam-based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks

TL;DR

This paper introduces a novel adversarial attack method targeting longitudinal mammogram-based breast cancer diagnosis models, revealing their vulnerability even when adversarial training is employed.

Contribution

We propose a feature-level relationship-based attack method for longitudinal mammogram models, demonstrating its effectiveness against state-of-the-art defenses.

Findings

Our attack outperforms existing methods in fooling diagnosis models.

The attack remains effective despite adversarial training.

Experiments on 590 patients validate the attack's efficacy.

Abstract

In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus enhancing the learning process over sequential imaging exams. Yet, the resilience of these longitudinal models against adversarial attacks remains underexplored. In this study, we proposed a novel attack method that capitalizes on the feature-level relationship between two sequential mammogram exams of a longitudinal model, guided by both cross-entropy loss and distance metric learning, to achieve significant attack efficacy, as implemented using attack transferring in a black-box attacking manner. We performed experiments on a cohort of 590 breast cancer patients (each has two sequential mammogram exams) in a case-control setting. Results showed that our proposed method surpassed several state-of-the-art adversarial attacks in fooling the diagnosis models to give opposite outputs. Our method remained effective even if the model was trained with the common defending method of adversarial training.