MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer

TL;DR

This paper introduces MIRST-DM, a novel training method combining multi-instance self-training and a drop-max layer to enhance adversarial robustness in small medical image datasets, specifically for breast cancer ultrasound classification.

Contribution

The paper proposes MIRST-DM, a new approach that improves adversarial robustness and generalizability on small datasets by using iterative adversarial instance generation and a drop-max layer.

Findings

Achieves state-of-the-art adversarial robustness on breast ultrasound data.

Effectively learns smoother decision boundaries with small datasets.

Robust against three prevalent adversarial attacks.

Abstract

Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models' generalizability. However, RST and other state-of-the-art defense approaches failed to preserve the generalizability and reproduce their good adversarial robustness on small medical image sets. In this work, we propose the Multi-instance RST with a drop-max layer, namely MIRST-DM, which involves a sequence of iteratively generated adversarial instances during training to learn smoother decision boundaries on small datasets. The proposed drop-max layer eliminates unstable features and helps learn representations that are robust to image perturbations. The proposed approach was validated using a small breast ultrasound dataset with 1,190 images. The results demonstrate that the proposed approach achieves state-of-the-art adversarial robustness against three prevalent attacks.