用小波分析分离病理图像中的干扰噪声，提升模型抗攻击能力。
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
- 基于哈尔小波的多层级频域正则化，区分干扰与真实组织结构。
- 在对抗攻击下鲁棒性提升10.69%，同时保持纹理和色彩真实度。
- 专为苏木精-伊红染色设计，适合临床病理影像安全应用。
深度学习在数字病理分析与癌症筛查中广泛应用，但神经网络易受对抗扰动影响，威胁其在临床中的可靠部署。在组织病理图像中，高频对抗噪声难以与细微但诊断相关的组织结构区分。为此，本文提出染色感知小波正则化（SAWR），一种基于哈尔变换的多层级小波域正则化框架，可分层解耦对抗扰动与诊断结构信息。该频域约束进一步针对不同组织染色通道进行优化，符合苏木精-伊红染色的生物学特性。集成至即时净化框架后，SAWR相较基线方法在对抗鲁棒性上提升最高达10.69%，并在对抗扰动下保持纹理与光谱保真度。
原文摘要 · Abstract (English)
Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of neural networks to adversarial perturbations raises safety concerns for reliable deployment in clinical practice. In histopathological images, this challenge is exacerbated by the difficulty of distinguishing high-frequency adversarial noise from subtle and diagnostically relevant tissue structures. To address this issue, we propose Stain-Aware Wavelet Regularization (SAWR), an adversarial purification framework that leverages multi-level wavelet-domain regularization based on Haar transform to hierarchically disentangle adversarial perturbations from diagnostic structural information. This spectral constraint is further extended to individual histological channels, enabling stain-specific frequency regulation consistent with the biological properties of Hematoxylin and Eosin. When integrated into an instant purification framework, SAWR improves adversarial robustness by up to 10.69\% over the baseline approach, while maintaining texture and spectral fidelity under adversarial perturbations.
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