# MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer Source: [MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer]() Collection index: [llms.txt]() Data updated: 2025-12-24T03:03:39.062Z Authors: Shoukun Sun, Min Xian, Aleksandar Vakanski, Hossny Ghanem Published: 2022-05-02T20:25:26.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()