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

• URL: http://arxiv.org/abs/2205.01674v1
• Date: Mon, 2 May 2022 20:25:26 GMT
• Title: MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
• Authors: Shoukun Sun, Min Xian, Aleksandar Vakanski, Hossny Ghanem
• Abstract summary: We propose the Multi-instance RST with a drop-max layer, namely MIRST-DM, to learn smoother decision boundaries on small datasets. The proposed approach was validated using a small breast ultrasound dataset with 1,190 images.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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